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Dissecting The Twitter Hack With A Cybersecurity Evangelist

Dissecting The Twitter Hack With A Cybersecurity Evangelist

Bottom Line: Shattering the false sense of security in tech, the recent Twitter hack blended altruism, fame, greed, social engineering via SIM swapping and insider threats to steal $120,000 from victims when the economic and political damage could have been far worse.

Targeting the most influential celebrities on Twitter, hackers orchestrated a social engineering-based attack Wednesday promoting a cryptocurrency scam. Business leaders, celebrities, politicians and billionaires’ accounts were hacked using Twitter’s administrative tools. Personal Twitter accounts hacked include those of Amazon CEO Jeff Bezos, Joe Biden, Tesla CEO Elon Musk, President Barack Obama, Bill Gates, Warren Buffet and others. Apple and Uber’s Twitter accounts were also hacked.

Using SIM swapping, in which threat actors trick, coerce or bribe employees of their victims to gain access to privileged account credentials and administrative tools, hackers were able first to change the email address of each targeted account. Next, two-factor authentication was turned off so when an alert was sent of the account change it went to the hacker’s email address. With the targeted accounts under their control, hackers began promoting their cryptocurrency scam. While not all details of the attack have surfaced Motherboard’s story of how hackers convinced a Twitter employee to help them the hijack accounts makes for fascinating reading.

Dissecting The Hack

Interested in dissecting the hack from a cybersecurity standpoint, I contacted Dr. Torsten George, Cybersecurity Evangelist and industry expert from Centrify. Torsten is also a leading authority on privileged access management and how to thwart breaches involving privileged access credentials.

Louis:  What was your initial impression upon breaking news of the hack and what did you believe would cause such a massive hack of celebrity and leading political figures accounts this past week?

Torsten: When the news broke, the media probably polled other security experts and the first initial reaction was, ‘Oh, that’s a massive attack, most likely a credential-based attack,’ because 80% of today’s data breaches go back to privilege access abuse. They are typically first triggered by phishing attacks, the precursor to many attacks where the attackers tried to capture these credentials and then leverage them to attack their victim’s organizations.

So, the breaking news indicated that most likely, somebody was able to leverage a compromised credential to enter into the Twitter environment and take over accounts. However, more and more information became available, with screenshots being shared of internal Twitter tools. For me, that raised a red flag, because in a typical attack pattern we’re seeing three distinct phases in the cyber-attack lifecycle: the compromise, the exploration phase and the exfiltration of sensitive data, which includes covering up tracks and potentially creating a backdoor for future attacks.

When performing reconnaissance, hackers commonly try to identify regular IT schedules, security measures, network traffic flows and scan the entire IT environment to gain an accurate picture of the network resources, privileged accounts and services. Domain controllers, Active Directory and servers are prime reconnaissance targets to hunt for additional privileged credentials and privileged access.

They wouldn’t necessarily look for administrative tools that could be leveraged for their attack unless they have intimate knowledge that those tools exist in the victim’s environment — be it by having worked for the company in the past or representing an insider threat.

Louis: What’s the anatomy of an insider attack, based on your experience?

Torsten: As was later confirmed by Twitter, it became very apparent that this is a case of insider threats, where you have an insider that has been leveraged for this attack. The most common insider threats can be defined by the intent and motivation of the individuals involved. The 2019 Verizon Insider Threat Report defines five distinct insider threats based on data breach scenarios and they all have excellent, accurate names: the Careless Worker, the Inside (often recruited) Agent, the Disgruntled Employee, the Malicious Insider and the Feckless Third-Party.

Considering the global environment we’re facing right now, with Covid-19 and other related economic hardships, the risk of insider threats is exacerbated, as pending furloughs or pay cuts may tempt employees to exfiltrate data to secure a new job or make up for income losses.

So a privileged administrator might be more open to people that approach them and say, ‘Would you be willing to share with us your access credentials, or would you do something on our behalf to exfiltrate data or to manipulate data?’ That risk has increased dramatically across all industries.

So it turned out the first suspicion was phishing attacks, followed by compromised credentials. It turns out to be an insider threat. Organizations need to be prepared for that.

Louis: What can companies do to reduce the likelihood a malicious insider will hack them?

Torsten: It becomes a little bit trickier when you deal with a malicious insider because they most likely know your environment, they might know your defense mechanisms and they might know the security tools that your likely using. So they can bypass these security controls and try to gain the control of data that they can then profit from.

Organizations have to rethink the way that they’ve structured their defense controls and truly take an approach of an in-depth strategy with a different layer of defenses. The first layer that comes to mind in this particular case is multi-factor authentication (MFA) which is still low-hanging fruit. There are still many organizations out there that are not taking advantage of implementing MFA.

While MFA is highly recommended, it isn’t as effective against insider threats because they have that second factor of authentication and can pass those challenges. Organizations need to go beyond MFA if they want to have a layered security strategy.

Louis: What are some of the ways they can go beyond MFA to avoid being the victim of an insider threat?

Torsten: A very important component of your defense strategy should be the approach of zero standing privileges, which is something Gartner recommends to its clients. That means that I have normal privileges and entitlements to do my job, like answering emails and using the Internet, but that’s probably all I need. If I need more access, I’ll have to elevate my privilege for the time needed to do that particular task but then rescind that privilege once it’s done.

If I have zero standing privileges – even if somebody compromises my credential, even if I’m an insider – I don’t have immediate access to the keys to the kingdoms to do whatever I want.

And before privilege elevation, organizations should require context through a formal request. For example, require the user to submit a ticket through ServiceNow or any other IT Service Management platform to detail what they need to access, for how long and to do what. That way, there is an auditing trail and an approval process. If the threat actor – whether insider or not – doesn’t do this they don’t get privileged access to that target system.

Louis: Besides those perhaps expected controls, what other controls might have helped in this particular scenario?

Torsten: Organizations should also take advantage of modern tools to leverage machine learning technology, so that looks at user behavior and risk factors to also get a hold of these insider attacks. All the other security controls are more tailored towards external preparation at first. Still, once you implement machine learning technology and user behavior analytics that’s where you also can capture insider threats.

Machine learning can look for suspicious activity, such as a target being accessed outside of a typical maintenance window, or is the administrator logging in from a different location or device than usual. It can then trigger an MFA request and also issue a real-time alert, regardless of whether the MFA challenge is successfully resolved.

Furthermore, in the case of Twitter, there are privacy and regulatory concerns that could also be additional triggers for real-time alerts and to shut down this activity automatically. Regulations like the CCPA (California Consumer Privacy Act) and GDPR (General Data Protection Regulation) mean that platforms like Twitter have to be very careful with any access to or manipulation of a customer’s feed. That could – and should have – instantly triggered a real-time alert when an administrator was posting on behalf of a user.

Louis: Do you think this is going to be the start of an entirely new era of hacks where hackers will pay off internal employees for promotional messages?

Torsten: Quite frankly, we have seen an uptick since the start of the Covid-19 pandemic. And I believe now that this Twitter attack has been covered in the press so much, you will have copycats that will try to do the same. Some of them will also target social media platforms, but others that might be a little bit smarter because social media is easily detectable if something goes wrong. An industry like healthcare could be a prime target and there is already news that Russian hackers are attacking healthcare providers and research labs to try to gain access to vaccine research.

Louis: Given how significant this hack is in terms of the progression or the growing sophistication of threats, what are the top three predictions you have for the rest of 2020?

Torsten: Ransomware is an example of a technique that has changed quite significantly in two ways. First, they are no longer only delivered via an email, but also via social media platforms, SMS messages and more. Second, ransomware is no longer only focused on shutting down business operations. The most recent example with EDP Renewables North American, a subsidiary of an European-based electric utilities company, showed that hackers leveraged ransomware to exfiltrate data. Not to lock it down, but to exfiltrate data and then ask for ransom from their victim to not publish the data on the Dark Web.

Second, as I’ve already covered, the current economic hardships of the pandemic will cause more people to jump on the bandwagon and become cybercriminals. And these aren’t the people you see in movies – dark characters in hoodies using sophisticated hacking techniques to breach the government. These are your neighbors, the little boys next door. For them it’s not a big deal to become a cyber-criminal.

Third, as you’d expect, the number of cyber-attacks will increase as a result and they will continue to find new and innovative ways to find the easiest way in. The Twitter incident taught us that there was no technology “breach” required. It was just finding the right person with the right privileges and paying them to do 25 Tweets. That’s an easy payday.

I think this whole crisis that we’re going through will see a major uptick in attacks from the traditional cyber hackers, but also from a whole bunch of newbies and greenhorns that will try out their luck and see if they can make a buck. Either by ransomware attacks, phishing attacks, social engineering or any combination thereof.

10 Ways AI Is Improving New Product Development

10 Ways AI Is Improving New Product Development

  • Startups’ ambitious AI-based new product development is driving AI-related investment with $16.5B raised in 2019, driven by 695 deals according to PwC/CB Insights MoneyTree Report, Q1 2020.
  • AI expertise is a skill product development teams are ramping up their recruitment efforts to find, with over 7,800 open positions on Monster, over 3,400 on LinkedIn and over 4,200 on Indeed as of today.
  • One in ten enterprises now uses ten or more AI applications, expanding the Total Available Market for new apps and related products, including chatbots, process optimization and fraud analysis, according to MMC Ventures.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace.

Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout. As AI and ML get more ingrained in new product development, the IoT platforms and ecosystems supporting smarter, more connected products need to make plans now how they’re going to keep up. Relying on technology alone, like many IoT platforms are today, isn’t going to be enough to keep up with the pace of change coming.   The following are 10 ways AI is improving new product development today:

  • 14% of enterprises who are the most advanced using AI and ML for new product development earn more than 30% of their revenues from fully digital products or services and lead their peers is successfully using nine key technologies and tools. PwC found that Digital Champions are significantly ahead in generating revenue from new products and services and more than a fifth of champions (29%) earn more than 30% of revenues from new products within two years of information. Digital Champions have high expectations for gaining greater benefits from personalization as well. The following graphic from Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.) compares Digital Champions’ success with AI and ML-based new product development tools versus their peers:

10 Ways AI Is Improving New Product Development

 

  • 61% of enterprises who are the most advanced using AI and ML (Digital Champions) use fully integrated Product Lifecycle Management (PLM) systems compared to just 12% of organizations not using AI/ML today (Digital Novices). Product Development teams the most advanced in their use of AL & ML achieve greater economies of scale, efficiency and speed gains across the three core areas of development shown below. Digital Champions concentrate on gaining time-to-market and speed advantages in the areas of Digital Prototyping, PLM, co-creation of new products with customers, Product Portfolio Management and Data Analytics and AI adoption:

10 Ways AI Is Improving New Product Development

  • AI is actively being used in the planning, implementation and fine-tuning of interlocking railway equipment product lines and systems.  Engineer-to-order product strategies introduce an exponential number of product, service and network options. Optimizing product configurations require an AI-based logic solver that can factor in all constraints and create a Knowledge Graph to guide deployment. Siemens’ approach to using AI to find the optimal configuration out of 1090 possible combinations provides insights into how AI can help with new product development on a large scale. Source: Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019.

10 Ways AI Is Improving New Product Development

  • Eliminating the roadblocks to getting new products launched starts with using AI to improve demand forecast accuracy. Honeywell is using AI to reduce energy costs and negative price variance by tracking and analyzing price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management getting solid returns across the new product development process. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

10 Ways AI Is Improving New Product Development

  • Relying on AI-based techniques to create and fine-tune propensity models that define product line extensions and add-on products that deliver the most profitable cross-sell and up-sell opportunities by product line, customer segment and persona. It’s common to find data-driven new product development and product management teams using propensity models to define the products and services with the highest probability of being purchased. Too often, propensity models are based on imported data, built-in Microsoft Excel, making their ongoing use time-consuming. AI is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model created in Microsoft Power BI.

10 Ways AI Is Improving New Product Development

  • AI is enabling the next generation of frameworks that reduce time-to-market while improving product quality and flexibility in meeting unique customization requirements on every customer order. AI is making it possible to synchronize better suppliers, engineering, DevOps, product management, marketing, pricing, sales and service to ensure a higher probability of a new product succeeding in the market. Leaders in this area include BMC’s Autonomous Digital Enterprise (ADE). BMC’s ADE framework shows the potential to deliver next-generation business models for growth-minded organizations looking to run and reinvent their businesses with AI/ML capabilities and deliver value with competitive differentiation enabled by agility, customer centricity and actionable insights. The ADE framework is capable of flexing and responding more quickly to customer requirements than competitive frameworks due to the following five factors: proven ability to deliver a transcendent customer experience; automated customer interactions and operations across distributed organizations; seeing enterprise DevOps as natural evolution of software DevOps; creating the foundation for a data-driven business that operates with a data mindset and analytical capabilities to enable new revenue streams; and a platform well-suited for adaptive cybersecurity. Taken together, BMC’s ADE framework is what the future of digitally-driven business frameworks look like that can scale to support AI-driven new product development. The following graphic compares the BMC ADE framework (left) and the eight factors driving digital product development as defined by PwC (right) through their extensive research. For more information on BMC’s ADE framework, please see BMC’s Autonomous Digital Enterprise site. For additional information on PwC’s research, please see the document Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.).

10 Ways AI Is Improving New Product Development

  • Using AI to analyze and provide recommendations on how product usability can be improved continuously. It’s common for DevOps, engineering and product management to run A/B tests and multivariate tests to identify the usability features, workflows and app & service responses customers prefer. Based on personal experience, one of the most challenging aspects of new product development is designing an effective, engaging and intuitive user experience that turns usability into a strength for the product. When AI techniques are part of the core new product development cycle, including usability, delivering enjoyable customer experiences, becomes possible. Instead of a new app, service, or device is a chore to use, AI can provide insights to make the experience intuitive and even fun.
  • Forecasting demand for new products, including the causal factors that most drive new sales is an area AI is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams, how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. AI and ML are proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
  • Designing the next generation of Nissan vehicles using AI is streamlining new product development, trimming weeks off new vehicle development schedules. Nissan’s pilot program for using AI to fast-track new vehicle designs is called DriveSpark. It was launched in 2016 as an experimental program and has since proven valuable for accelerating new vehicle development while ensuring compliance and regulatory requirements are met. They’ve also used AI to extend the lifecycles of existing models as well. For more information, see the article, DriveSpark, “Nissan’s Idea: Let An Artificial Intelligence Design Our Cars,” September 2016.
  • Using generative design algorithms that rely on machine learning techniques to factor in design constraints and provide an optimized product design. Having constraint-optimizing logic within a CAD design environment helps GM attain the goal of rapid prototyping. Designers provide definitions of the functional requirements, materials, manufacturing methods and other constraints. In May 2018, General Motors adopted Autodesk generative design software to optimize for weight and other key product criteria essential for the parts being designed to succeed with additive manufacturing. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight component design. Please see the Harvard Business School case analysis, Project Dreamcatcher: Can Generative Design Accelerate Additive Manufacturing? for additional information.

Additional reading:

2020 AI Predictions, Five ways to go from reality check to real-world payoff, PwC Consulting

Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in)

AI Priorities February 2020 5 ways to go from reality check to real-world pay off, PwC, February, 2020 (PDF, 16 pp.)

Anderson, M. (2019). Machine learning in manufacturing. Automotive Design & Production, 131(4), 30-32.

Bruno, J. (2019). How the IIoT can change business models. Manufacturing Engineering, 163(1), 12.

Digital Factories 2020: Shaping The Future Of Manufacturing, PwC DE., 2017 (PDF, 48 pp.)

Digital Product Development 2025: Agile, Collaborative, AI Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.)

Enabling a digital and analytics transformation in heavy-industry manufacturing, McKinsey & Company, December 19, 2019

Global Digital Operations 2018 Survey, Strategy&, PwC, 2018

Governance and Management Economics, 7(2), 31-36.

Greenfield, D. (2019). Advice on scaling IIoT projects. ProFood World

Hayhoe, T., Podhorska, I., Siekelova, A., & Stehel, V. (2019). Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems and AI-driven decision-making. Journal of Self-

Industry’s fast-mover advantage: Enterprise value from digital factories, McKinsey & Company, January 10, 2020

Kazuyuki, M. (2019). Digitalization of manufacturing process and open innovation: Survey results of small and medium-sized firms in japan. St. Louis: Federal Reserve Bank of St Louis.

‘Lighthouse’ manufacturers lead the way—can the rest of the world keep up?  McKinsey & Company, January 7, 2019

Machine Learning in Manufacturing – Present and Future Use-Cases, Emerj Artificial Intelligence Research, last updated May 20, 2019, published by Jon Walker

Machine learning, AI are most impactful supply chain technologies. (2019). Material Handling & Logistics

MAPI Foundation, The Manufacturing Evolution: How AI Will Transform Manufacturing & the Workforce of the Future by Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 pp., opt-in)

Mapping heavy industry’s digital-manufacturing opportunities, McKinsey & Company, September 24, 2018

McKinsey, AI in production: A game changer for manufacturers with heavy assets, by Eleftherios Charalambous, Robert Feldmann, Gérard Richter and Christoph Schmitz

McKinsey, Digital Manufacturing – escaping pilot purgatory (PDF, 24 pp., no opt-in)

McKinsey, Driving Impact and Scale from Automation and AI, February 2019 (PDF, 100 pp., no opt-in).

McKinsey, ‘Lighthouse’ manufacturers, lead the way—can the rest of the world keep up?,by Enno de Boer, Helena Leurent and Adrian Widmer; January, 2019.

McKinsey, Manufacturing: Analytics unleashes productivity and profitability, by Valerio Dilda, Lapo Mori, Olivier Noterdaeme and Christoph Schmitz, March, 2019

McKinsey/Harvard Business Review, Most of AI’s business uses will be in two areas,

Morey, B. (2019). Manufacturing and AI: Promises and pitfalls. Manufacturing Engineering, 163(1), 10.

Preparing for the next normal via digital manufacturing’s scaling potential, McKinsey & Company, April 10, 2020

Reducing the barriers to entry in advanced analytics. (2019). Manufacturing.Net,

Scaling AI in Manufacturing Operations: A Practitioners Perspective, Capgemini, January, 2020

Seven ways real-time monitoring is driving smart manufacturing. (2019). Manufacturing.Net,

Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019

Smart Factories: Issues of Information Governance Manufacturing Policy Initiative School of Public and Environmental Affairs Indiana University, March 2019 (PDF, 68 pp., no opt-in)

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Team predicts the useful life of batteries with data and AI. (2019, March 28). R & D.

The AI-powered enterprise: Unlocking the potential of AI at scale, Capgemini Research, July 2020

The Future of AI and Manufacturing, Microsoft, Greg Shaw (PDF, 73 pp., PDF, no opt-in).

The Rise of the AI-Powered Company in the Postcrisis World, Boston Consulting Group, April 2, 2020

Top 8 Data Science Use Cases in Manufacturing, ActiveWizards: A Machine Learning Company Igor Bobriakov, March 12, 2019

Walker, M. E. (2019). Armed with analytics: Manufacturing as a martial art. Industry Week

Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.

Zulick, J. (2019). How machine learning is transforming industrial production. Machine Design

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it’s time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights.

The greater the accuracy and speed of supply chain-based data integration and knowledge, the greater the accuracy of custom product orders. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case.

Why Channels Need AI-Based Knowledge Sharing Networks Now

Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy. AI-based services, including Amazon Alexa, Microsoft Cortana, and Google Voice and others, rely on knowledge-sharing networks to collaborate with automotive supply chains and strengthen OEM partnerships. The following graphic reflects how successful Amazon’s Alexa Automotive OEM sales team is at using knowledge-sharing networks to gain design wins across their industry.

The following are a few of the many reasons why creating and continually fine-tuning an AI-based knowledge-sharing network is an evolving strategy worth paying attention to:

  • Supply chains are the primary source of knowledge that must permeate an organization’s structure and channels for the company to stay synchronized to broader market demands. For CPQ channel selling strategies to thrive, they need real-time pricing, availability, available-to-promise, and capable-to-promise data to create accurate, competitive quotes that win deals. The better the supplier collaboration across supply chains and with channel partners, the higher the probability of selling more. A landmark study of the Toyota Production System by Professors Jeffrey H Dyer & Kentaro Nobeoka found that Toyota suppliers value shared data more than cash, making knowledge sharing systems invaluable to them (Dyer, Nobeoka, 2000).
  • Smart manufacturing metrics also need to be contributing real-time data to knowledge sharing systems channel partners use, relying on AI to create quotes for products that can be built the fastest and are the most attractive to each customer. Combining manufacturing’s real-time monitoring data stream of ongoing order progress and production availability with supply chain pricing, availability, and quality data all integrated to a cloud-based CPQ platform gives channel partners what they need to close deals now. AI-based knowledge-sharing networks will link supply chains, manufacturing plants, and channel partners to create smart factories that drive more sales. According to a recent Capgemini study, manufacturers are planning to launch 40% more smart factories in the next five years, increasing their annual investments by 1.7 times compared to the previous three years, according to their recent Smart factories @ scale Capgemini survey. The following graphic illustrates the percentage growth of smart factories across key geographic regions, a key prerequisite for enabling AI-based knowledge-sharing networks with real-time production data:
  • By closing the data gaps between suppliers, manufacturing, and channels, AI-based knowledge-sharing networks give resellers the information they need to sell with greater insight. Amazon’s Alexa OEM marketing teams succeeded in getting the majority of design-in wins with automotive manufacturers designing their next-generation of vehicles with advanced electronics and AI features. The following graphic from Dr. Dyer’s and Nobeoka’s study defines the foundations of a knowledge-sharing network. Applying AI to a mature knowledge-sharing network creates a strong network effect where every new member of the network adds greater value.
  • Setting the foundation for an effective knowledge sharing network needs to start with platforms that have AI and machine learning designed in with structure that can flex for unique channel needs. There are several platforms capable of supporting AI-based knowledge-sharing networks available, each with its strengths and approach to adapting to supply chain, manufacturing, and channel needs. One of the more interesting frameworks not only uses AI and machine learning across its technology pillars but also takes into consideration that a company’s operating model needs to adjust to leverage a connected economy to adapt to changing customer needs. BMC’s Autonomous Digital Enterprise (ADE) is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to create innovation ecosystems in a knowledge-sharing network. Knowledge-sharing networks thrive on continuous learning. It’s good to see major providers using adaptive and machine learning to strengthen their platforms, with BMC’s Automated Mainframe Intelligence (AMI) emerging as a leader. Their approach to using adaptive learning to maintain data quality during system state changes and link exceptions with machine learning to deliver root cause analysis is prescient of where continuous learning needs to go.  The following graphic explains the ADE’s structure.

Conclusion

Knowledge-sharing networks have proven very effective in improving supply chain collaboration, supplier quality, and removing barriers to better inventory management. The next step that’s needed is to extend knowledge-sharing networks to resellers and enable knowledge sharing applications that use AI to tailor product and service recommendations for every customer being quoted and sold to. Imagine resellers being able to create quotes based on the most buildable products that could be delivered in days to buying customers. That’s possible using a knowledge-sharing network. Amazon’s success with Alexa design wins shows how their use of knowledge-sharing systems helped to provide insights needed across automotive OEMs wanted to add voice-activated AI technology to their next-generation vehicles.

References

BMC, Maximizing the Value of Hybrid IT with Holistic Monitoring and AIOps (10 pp., PDF).

BMC Blogs, 2019 Gartner Market Guide for AIOps Platforms, December 2, 2019

Cai, S., Goh, M., De Souza, R., & Li, G. (2013). Knowledge sharing in collaborative supply chains: twin effects of trust and power. International journal of production Research51(7), 2060-2076.

Capgemini Research Institute, Smart factories @ scale: Seizing the trillion-dollar prize through efficiency by design and closed-loop operations, 2019.

Columbus, L, The 10 Most Valuable Metrics in Smart Manufacturing, Forbes, November 20, 2020

Jeffrey H Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.

Myers, M. B., & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan Management Review49(4), 67.

Wang, C., & Hu, Q. (2020). Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation94, 102010.

 

How Barclays Is Preventing Fraud With AI

How Barclays Is Preventing Fraud With AI

Bottom Line: Barclays’ and Kount’s co-developed new product, Barclays Transact reflects the future of how companies will innovate together to apply AI-based fraud prevention to the many payment challenges merchants face today.

Merchant payment providers have seen the severity, scope, and speed of fraud attacks increase exponentially this year. Account takeovers, card-not-present fraud, SMS spoofing, and phishing are just a few of the many techniques cybercriminals are using to defraud merchants out of millions of dollars. One in three merchants, 32%, prioritize payment providers’ fraud and security strengths over customer support and trust according to a recent YouGov survey.  But it doesn’t have to be a choice between security and a frictionless transaction.

Frustrated by the limitations of existing fraud prevention systems, many payment providers are working as fast as they can to pilot AI- and machine-learning-based applications and platforms. Barclays Payment Solutions’ decision to work with AI-based solution Kount is what the future of AI-based fraud prevention for payment providers looks like.

How AI Helps Thwart Fraud And Increase Sales at Barclays   

Barclays Payment Services handles 40% of all merchant payments in the UK. They’ve been protecting merchants and their customers’ data for over 50 years, and their fraud and security teams have won industry awards. For Barclays, excelling at merchant and payment security is the only option.

In order to offer an AI-based suite of tools to help merchants make their online transactions both simpler and safer, Barclays chose to partner with Kount. Their model of innovating together enables Barclays to strengthen their merchant payment business with AI-based fraud prevention and gain access to Kount’s Identity Trust Global Network, the largest network of trust and fraud-related signals. Kount gains knowledge into how they can fine-tune their AI and machine learning technologies to excel at payment services. Best of all, Barclays’ merchant customers will be able to sell more by streamlining the payment experience for their customers. The following is an overview of the Barclays Transact suite for merchants.

Barclays and Kount defined objectives for Barclay Transact: protect against increasingly sophisticated eCommerce fraud attempts, improve their merchants’ customer experiences during purchases, prepare for UK-mandated Strong Customer Authentication (SCA) by allowing businesses to take advantage of Transaction Risk Analysis (TRA) exemptions, optimize payment acceptance workflows and capitalize on Kount’s Identity Trust Global Network.

Adding urgency to the co-creation of Barclays Transact are UK regulatory requirements. To help provide clarity and support to merchants and the market from the impact of Covid-19 the Financial Conduct Authority (FCA) have agreed to delay the enforcement of a Strong Customer Authentication (SCA) until 14 September 2021 in the UK. The European Economic Area (EEA) deadline remains 31 December, 2020. Kount’s AI- and machine learning algorithms designed into Barclay Transact, tested at beta sites and fine-tuned for the first release, are effective in meeting UK government mandates.

How AI Is Turning Trust Into A Sales Accelerator At Barclays

The Barclays Payment Solutions and Kount teams believe that the more ambitious the goals for Barclays Transact to deliver value to merchants, the stronger the suite will be. Here are examples of goals businesses can achieve with this partnership:

  1. Achieve as few false positives as possible by making real-time updates to machine learning algorithms and fine-tuning merchant responses.
  2. Reduce the number of manual reviews for fraud analysts consistently by applying AI and machine learning to provide early warning of anomalies.
  3. Minimize the number of chargebacks to merchant partners.
  4. Reduce the friction and challenges merchants experience with legacy fraud prevention systems by streamlining the purchasing experience.
  5. Enable compliance to UK-mandated regulatory requirements while streamlining merchants and their customers’ buying experiences.

Barclays Transact analyzes every transaction in real-time using Kount’s AI-based fraud analysis technology, scoring each on a spectrum of low to high risk. Each Barclays merchant’s gateway then uses this score to identify the transactions which qualify for TRA exemptions. This results in a more frictionless payment and checkout experience for customers, resulting in lower levels of shopping cart abandonment and increased sales. Higher-risk transactions requiring further inspection will still go through two-factor authentication, or be immediately declined, per the regulation and customer risk appetite. The following is an example of the workflow Barclays and Kount were able to accomplish by innovating together:

Conclusion 

Improving buying experiences and keeping them more secure on a trusted platform is an ambitious design goal for any suite of online tools. Barclays and Kount’s successful development and launch of a co-developed product is prescient and points the way forward for payment providers who need AI expertise to battle fraud now. A bonus is how the partnership is going to enrich the Kount Identity Trust Global Network, the largest network of trust and risk signals, which is comprised of 32 billion annual interactions from more than 6,500 customers across 75+ industries. “We are excited to be partnering with Kount, because they share our goal of collaborative innovation, and a drive to deliver best-in-class shopper experiences. Thanks to Kount’s award-winning fraud detection software, the new module will not only help customers to fight fraud and prevent unwanted chargebacks, but it will also help them to maximize sales, improve customer experience, and better prepare for the introduction of SCA,” David Jeffrey, Director of Product, Barclaycard Payments said.

10 Ways Enterprises Are Getting Results From AI Strategies

10 Ways Enterprises Are Getting Results From AI Strategies

  • One in 10 enterprises now use 10 or more AI applications; chatbots, process optimization, and fraud analysis lead a recent survey’s top use cases according to MMC Ventures.
  • 83% of IT leaders say AI & ML is transforming customer engagement, and 69% say it is transforming their business according to Salesforce Research.
  • IDC predicts spending on AI systems will reach $97.9B in 2023.

AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing.

What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value. There are various frameworks available, with BMC’s Autonomous Digital Enterprise (ADE) encapsulating what enterprises need to scale out their AI pilots into production. What’s unique about BMC’s approach is its focus on delivering transcendent customer experiences by creating an ecosystem that uses technology to cater to every touchpoint on a customer’s journey, across any channel a customer chooses to interact with an enterprise on.

10 Areas Where AI Is Delivering Proven Value Today

Having progressed from pilot to production across many of the world’s leading enterprises, they’re great examples of where AI is delivering value today. The following are 10 areas where AI is delivering proven value in enterprises today

  • Customer feedback systems lead all implementations of AI-based self-service platforms. That’s consistent with the discussions I’ve had with manufacturing CEOs who are committed to Voice of the Customer (VoC) programs that also fuel their new product development plans. The best-run manufacturers are using AI to gain customer feedback better also to improve their configure-to-order product customization strategies as well. Mining contact center data while improving customer response times are working on AI platforms today. Source: Forrester study, AI-Infused Contact Centers Optimize Customer Experience Develop A Road Map Now For A Cognitive Contact Center.
  • McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, overall inventory reductions of 20% to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? McKinsey & Company.

10 Ways Enterprises Are Getting Results From AI Strategies

  • The majority of CEOs and Chief Human Resource Officers (CHROs) globally plan to use more AI within three years, with the U.S. leading all other nations at 73%. Over 63% of all CEOs and CHROs interviewed say that new technologies have a positive impact overall on their operations. CEOs and CHROs introducing AI into their enterprises are doing an effective job at change management, as the majority of employees, 54%, are less concerned about AI now that they see its benefits. C-level executives who are upskilling their employees by enabling them to have stronger digital dexterity skills stand a better chance of winning the war for talent. Source: Harris Interactive, in collaboration with Eightfold Talent Intelligence And Management Report 2019-2020 Report.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where AI can contribute to solving complex constraints, cost, and delivery problems manufacturers are facing today. For example, AI is providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).
  • A semiconductor manufacturer is combining smart, connected machines with AI to improve yield rates by 30% or more, while also optimizing fab operations and streamlining the entire production process. They’ve also been able to reduce supply chain forecasting errors by 50% and lost sales by 65% by having more accurate product availability, both attributable to insights gained from AI. They’re also automating quality testing using machine learning, increasing defect detection rates up to 90%. These are the kind of measurable results manufacturers look for when deciding if a new technology is going to deliver results or not. These and many other findings from the semiconductor’s interviews with McKinsey are in the study, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? . The following graphic from the study illustrates the many ways AI and machine learning are improving semiconductor manufacturing.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
  • AI is reducing logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration, when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra.
  • Detecting and acting on inconsistent supplier quality levels and deliveries using AI-based applications is reducing the cost of bad quality across electronic, high-tech, and discrete manufacturing. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. Using AI, manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery, and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour.

10 Ways Enterprises Are Getting Results From AI Strategies

  • Optimizing Shop Floor Operations with Real-Time Monitoring and AI is in production at Hitachi today. Combining real-time monitoring and AI to optimize shop floor operations, providing insights into machine-level loads and production schedule performance, is now in production at Hitachi. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using AI.  Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics, and AI Are Making Factories Intelligent and Agile, Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D.

10 Ways Enterprises Are Getting Results From AI Strategies

Additional reading:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF,

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

2018 (43 pp., PDF, free, no opt-in).

Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in)

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

bes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control28(11/12), 873-876.

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains (PDF, 22 pgs., no opt-in)

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

 

What You Need To Know About Location Intelligence In 2020

What You Need To Know About Location Intelligence In 2020

  • 53% of enterprises say that Location Intelligence is either critically important or very important to achieving their goals for 2020.
  • Leading analytics and platform vendors who offer Location Intelligence include Alteryx, Microsoft, Qlik, SAS, Tableau and TIBCO Software.
  • Location Intelligence vendors providing specialized apps and platforms include CARTO, ESRI, Galigeo, MapLarge, and Pitney Bowes.
  • Product Managers need to consider how adding Location Intelligence can improve the contextual accuracy of marketing, sales, and customer service apps and platforms.
  • Marketers need to look at how they can capitalize on smartphones’ prolific amounts of location data for improving advertising, buying, and service experiences for customers.
  • R&D, Operations, and Executive Management lead all other departments in their adoption and use of Location Intelligence this year.
  • Enterprises favor cloud-based Location Intelligence deployments in 2020, with on-premise deployments also seeing new sales this year.

These and many other fascinating insights are from Dresner Advisory Services’ 2020 Location Intelligence Market Study, their 7th annual report that examines enterprise end-users’ requirements and features including geocoding support, location intelligence visualization, analytics capabilities, and third-party GIS integration. The study is noteworthy for its depth of insights into industry adoption of Location Intelligence and how user requirements drive industry capabilities. Dresner Advisory Services defines location intelligence as a form of Business Intelligence (BI), where the dominant dimension used for analysis is location or geography. Most typically, though not exclusively, analyses are conducted by viewing data points overlaid onto an interactive map interface.

“When we began covering Location Intelligence in 2014, we saw the potential for the topic to gain mainstream interest,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “With the growth in visualization and the emergence of the Internet of Things (IoT), incorporating maps and location into business analyses have become increasingly important to many organizations.” Please see page 11 for a description of the methodology and page 13 for an overview of study demographics. Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors.

Key insights from the study that provides an excellent background on the current state of location intelligence in 2020 include the following:

  • R&D, Operations, and Executive Management lead all enterprise areas in adoption with Location Intelligence being considered critical to their ongoing operations. The majority of Marketing & Sales leaders see Location Intelligence as very important to their ongoing operations. The following graphic compares how important Location Intelligence is to each of the seven departments included in the survey:
  • 90% of Government organizations consider Location Intelligence to be critical or very important to their ongoing operations. Healthcare providers have the second-highest number of organizations who rate Location Intelligence as critical. The study found that mean importance levels are similar across Business Services, Financial Services, Manufacturing, and Consumer Services organizations and decline further among Technology, Retail/Wholesale, and Higher Education segments.
  • Data visualization/mapping dominates all other Location Intelligence use cases in 2020, with over 70% of organizations considering it critical or very important to accomplishing their goals. The study found that the majority of other use cases haven’t achieved the broad adoption data visualization & mapping has. Despite the lower levels of criticality assigned to the nine other use cases, they each show the potential to streamline essential marketing, sales, and operational areas of an enterprise. Site planning/site selection, geomarketing, territory management/optimization, and logistics optimization make up a tier of secondary interest that taken together streamlines supply chains while making an organization easier to buy from. The Dresner research team also defines the third tier of use cases led by fleet routing and citizen services, followed by IoT & smart cities, indoor mapping, and real estate investment/pricing analysis. Despite IoT being over-promoted by vendors, just over 50% of enterprises say the technology is not important to them at this time. The following graphic compares Location Intelligence use cases by the level of criticality as defined by responding organizations:
  • R&D leads all departments in data visualization/mapping adoption, reflecting the high level of importance this use case has across entire enterprises as well. Additional departments and functional areas relying on data visualization/mapping include Operations, Business Intelligence Competency Center (BICC), and Executive Management. Geomarketing is seeing the most significant adoption in Marketing & Sales. Operations lead all other functional areas in the adoption of logistics optimization and fleet routing use cases. Dresner’s research team found that R&D’s interest in Location Intelligence, which varies across use cases, may reflect the use of packaged applications as well as select custom development.
  • Map-based visualization, dashboard inclusion of maps, and drill-down navigation through map interfaces are the three highest priority features enterprises look for today. These three features are considered very important to between 64% to 67% of leaders interviewed. Layered visualizations, multi-layer support, and custom region definition are the next most important features. The following graphic provides an overview of prioritized Location intelligence visualization features.
  • Executive Management, BICC, and Operations have the highest level of interest in map-based visualizations that further accelerate the adoption of Location Intelligence across enterprises. Executive Management also leads all others in their interest in dashboard inclusion of maps and custom map support. Executive Management’s increasing adoption of multiple Location intelligence use cases is a catalyst driving greater enterprise-wide adoption. R&D’s prioritizing the layering of visualizations on top of maps, offline mapping and animation of data on maps are leading indicators of these use cases attaining greater enterprise adoption in future years.
  • Four of the top ten Location Intelligence features are considered very important/critical to enterprises, reflecting a maturing market. The most popular (counting, quantifying, or grouping) is critical or very important to 46% of organizations and at least important to nearly 70%. Another indicator of how quickly Location Intelligence is maturing in enterprises is the advanced nature of analytics features being relied on today. Predicting trends and volatility, detecting clusters and outliers, and measuring distances reflect how multiple departments in enterprises are collaborating using Location Intelligence to achieve their shared goals.
  • Government dominates the use of data visualization/mapping with a strong interest in site planning/site selection, citizen services, fleet routing, and territory management. Business Services are most interested in using Location Intelligence for Indoor Mapping and IoT & Smart Cities. Geomarketing is the most adopted feature in Higher Education, Financial Services, Healthcare, and Retail/Wholesale. Manufacturing and Retail/Wholesale lead all other industries in their adoption of Logistics Optimization. The following graphic provides insights into Location Intelligence use case by industry:
  • Executive Management and Business Intelligence Competency Centers (BICC) most prioritize Location Intelligence applications that have built-in or native geocoding. Enterprises are looking at how built-in or native geocoding can scale across their Location Intelligence use cases and broader BI strategy with Executive Management taking the lead on achieving this goal. Automated geocoding support and street-level geocoding support are also a high priority to Executive Management. Marketing/Sales lead all other departments in their interest in geofencing/reverse geofencing, indicating enterprises are beginning to use these geocoding features to achieve greater accuracy in their marketing and selling strategies. It’s interesting to note that geofencing/reverse geofencing has progressed from R&D in previous studies to Marketing/Sales putting the highest priority on it today. Dresner’s research team interprets the shift to customer-facing strategies being an indicator of broader enterprise adoption for geofencing/reverse geofencing.
  • 61% of organizations say Google integration is essential to their Location Intelligence strategies. Google continues to dominate organizations’ roadmaps as the integration of choice for adding more GIS data to Location Intelligence strategies. ESRI is the second choice with 45% of organizations naming it as an integration requirement. Database extensions (30%) are the next most cited, followed by OpenStreetMap (20%). All other choices are requirements at less than 20% of organizations.

How To Redefine The Future Of Fraud Prevention

How To Redefine The Future Of Fraud Prevention

Bottom Line: Redefining the future of fraud prevention starts by turning trust into an accelerator across every aspect of customer lifecycles, basing transactions on identity trust that leads to less friction and improved customer experiences.

Start By Turning Trust Into A Sales & Customer Experience Accelerator

AI and machine learning are proving to be very effective at finding anomalies in transactions and scoring, which are potentially the most fraudulent. Any suspicious transaction attempt leads to more work for buying customers to prove they are trustworthy. For banks, e-commerce sites, financial institutes, restaurants, retailers and many other online businesses, this regularly causes them to lose customers when a legitimate purchase is being made, and trusted customer is asked to verify their identity. Or worse, a false positive that turns away a good customer all together damages both that experience and brand reputation.

There’s a better way to solve the dilemma of deciding which transactions to accept or not. And it needs to start with finding a new way to establish identity trust so businesses can deliver better user experiences. Kount’s approach of using their Real-Time Identity Trust Network to calculate Identity Trust Levels in milliseconds reduces friction, blocks fraud, and delivers an improved user experience. Kount is capitalizing on their database that includes more than a decade of trust and fraud signals built across industries, geographies, and 32 billion annual interactions, combined with expertise in AI and machine learning to turn trust into a sales and customer experience multiplier.

How Real-Time AI Linking Leads To Real-Time Identity Trust Decisions

Design In Identity Trust So It’s The Foundation of Customer Experience

From an engineering and product design standpoint, the majority of fraud prevention providers are looking to make incremental gains in risk scoring to improve customer experiences. None, with the exception of Kount, are looking at the problem from a completely different perspective, which is how to quantify and scale identity trust. Kount’s engineering, product development, and product management teams are concentrating on how to use their AI and machine learning expertise to quantify real-time identity trust scores that drive better customer experiences across the spectrum of trust. The graphic below illustrates how Kount defines more personalized user experiences, which is indispensable in turning trust into an accelerator.

An Overview of Kount’s Technology Stack

How To Redefine The Future Of Fraud Prevention

Realize Trust Is the Most Powerful Revenue Multiplier There Is

Based on my conversations with several fraud prevention providers, they all agree that trust is the most powerful accelerator there is to reducing false positives, friction in transactions, and improving customer experiences. They all agree trust is the most powerful revenue multiplier they can deliver to their customers, helping them reduce fraud and increase sales. The challenge they all face is quantifying identity trust across the wide spectrum of transactions their customers need to fulfill every day.

Kount has taken a unique approach to identity trust that puts the customer at the center of the transactions, not just their transactions’ risk score. By capitalizing on the insights gained from their Identity Trust Global Network, Kount can use AI and machine learning algorithms to deliver personalized responses to transaction requests in milliseconds. Using both unsupervised and supervised machine learning algorithms and techniques, Kount can learn from every customer interaction, gaining new insights into how to fine-tune identity trust for every customer’s transaction.

In choosing to go in the direction of identity trust in its product strategy, Kount put user experiences at the core of their platform strategy. By combining adaptive fraud protection, personalized user experience, and advanced analytics, Kount can create a continuously learning system with the goal of fine-tuning identity trust for every transaction their customers receive. The following graphic explains their approach for bringing identity trust into the center of their platform:

Putting Customers & Their Experiences First Is Integral To Succeeding With Identity Trust

How To Redefine The Future Of Fraud Prevention

 

Improving customer experiences needs to be the cornerstone that drives all fraud prevention product and services road maps in 2020 and beyond. And while all fraud prevention providers are looking at how to reduce friction and improve customer experiences with fraud scoring AI-based techniques, their architectures and approaches aren’t going in the direction of identity trust. Kount’s approach is, and it’s noteworthy because it puts customer experiences at the center of their platform. How to redefine the future of fraud prevention needs to start by turning trust into a sales and customer experience accelerator, followed by designing in identity trust. Hence, it’s the foundation of all customer experiences. By combining the power of networked data and adaptive AI and machine learning, more digital businesses can turn trust into a revenue and customer experience multiplier.

10 Ways AI Is Going To Improve Fintech In 2020

Bottom Line: AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools.

Zest.ai’s 2020 Predictions For AI In Credit And Lending captures the gradual improvements I’ve also been seeing across Fintech, especially at the tech stack level. Fintech startups, enterprise software providers, and the investors backing them believe cloud-based payments, lending, and insurance apps are must-haves to drive future growth. Combined with Internet & public cloud infrastructure and mobile apps, Fintech is evolving into a fourth platform that provides embedded financial services to any business needing to subscribe to them, as Matt Harris of Bain Capital Ventures writes in Fintech: The Fourth Platform – Part Two. Embedded Fintech has the potential to deliver $3.6 trillion in market value, according to Bain’s estimates, surpassing the $3 trillion in value created by cloud and mobile platforms. Accenture’s recent survey of C-suite executives’ adoption and plans found that 84% of all executives believe they won’t achieve their growth objectives unless they scale AI, and 75% believe they risk going out of business in 5 years if they don’t. The need to improve payment, lending and insurance combined with customers’ mercurial preferences for how they use financial services are challenges that AI and machine learning (ML) are solving today.

How AI & Machine Learning Will Improve Fintech In 2020

Fintech’s traditional tech stacks weren’t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What’s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI & machine learning are proving to be very effective at interpreting and recommending actions based on real-time data streams. They’re also improving customer experiences and reducing risk, two additional factors motivating lenders to upgrade their traditional tech stacks with proven new technologies.

The following are ten predictions of how AI will improve FinTech in 2020, thank you Zest.ai for your insights and sharing your team’s expertise on these:

  1. Zest predicts lenders will increase the use of ML as the way to grow into the no-file/thin-file segments, especially rising Gen Zers with little to no credit history. Traditional tech stacks make it difficult to find and grow new borrower pools. Utah-based auto lenderPrestige Financial Services chose to rely on an AI solution instead. The chose Zest AI to find and cultivate a borrower pool of people in the 19-35 age group. Using an AI-based loan approval workflow, Prestige was able to increase loan approval rates by 25%, and for people under 20 by threefold.
  2. Mortgage lenders’ adoption of AI for finding qualified first-time homeowners is going to increase as more realize Gen Z (23 – 36-year-olds) are the most motivated of all to purchase a home. In 2020, long-standing assumptions about first-time homebuyers and their motivations are going to change. A recent story in HousingWire, “This generation is the most willing to do whatever it takes to buy a home,” explains that Gen Z, or those people born between 1996 and 2010, are the most likely to relocate to purchase a new home. A recent TransUnion market analysis found 70% of Gen Z prospective home buyers are willing to relocate to buy their first home, leading all active generations. 65% of Gen Xers, or those born between 1965 to 1980, were the second most likely to move. AI and ML can help lenders more precisely target potential Gen Z first-time homebuyers, measuring the impact of their marketing campaigns on attracting new borrowers. The TransUnion market analysis finds that 58% of respondents are delaying a home purchase due to anticipated high down payments or monthly payments. 51% said the need to obtain a 10% to 20% down payment was stopping them. According to Joe Mellman, TransUnion senior vice president, and mortgage business leader, “Many of our potential first-time homebuyer respondents don’t seem to be aware of the wide variety of financing options available to them.” The TransUnion market analysis found that many of the potential first-time homeowner respondents have never heard of low down-payment options from Fannie Mae, Freddie Mac, or of the Federal Housing Administration.
  3. Zest predicts banks and other financial institutions will strengthen their business cases for AI pilots and production-level deployments by recognizing the operating expense (OPEX) savings of ML. Several recurring costs involved in developing, validating and deploying credit risk models can be reduced or cut by switching to machine learning, according to Zest. Lenders can get the most out of their data acquisition spending by using modern ML tools to assess which data sources yield the most predictive power for a model. Lenders will also switch to ML to simplify their IT and risk operations by consolidating into fewer models that can do the work of what used to be multiple individual linear models for every customer segment.
  4. Compliance cost growth will decline even faster due to ML. Financial institutions that have AI/ML algorithms in production log every change in a model and can produce all the required model risk governance documents in minutes instead of a compliance team manually taking weeks to do it. Automated tools also shrink the time it takes to do fair lending testing by building less discriminatory models on the fly rather than the time-intensive approach of drop-one-variable-and-test. Time is money, especially in lending.
  5. AI and ML will gain critical mass in collections, providing insights into which approach is the most effective for a given customer. Zest has built collections models for a few financial services firms and has found them to be very effective. Collections logic, predicting which customers to wait on when bills are past due, is a strong fit for machine learning. With one bank, Zest found that ML models can, for example, accurately target the borrowers most likely to make a certain minimum payment based on the value of their loan within 60 days of falling behind their due date. In three months, Zest built two models from traditional credit bureaus and the bank’s proprietary collections metrics to predict this repayment propensity of borrowers. One insight into the data was that borrower behavior accounted for just over half of the bank’s ability to collect missed payments, but operations played a significant role.
  6. If there’s a downturn, ML will get blamed (even though it can actually help in a downturn). Pankaj Kulshreshtha, CEO of Scienaptics, originally made this observation at the Money 20/20 Conference held earlier this year. Models built only in good times can see their correlations break when times go bad. Lenders who observe best practices in AI and ML adoption will make sure to stress-test their models, perhaps by including synthetic data to add heterogeneity. Better ML monitoring will be important, too. “ML models and algorithmic monitors can do a better job seeing around corners, spotting rising numbers of inbound outlier applicants that signal more volatile conditions ahead,” says Seth Silverstein, Executive Vice President of Credit Risk Analytics for Zest AI.  An effective ML monitoring tool should excel at spotting outlier applicants and feature drift, ensuring more accurate model outcomes.
  7. 2020 is going to be a break-out year for partnerships and co-opetition as payments, lending and insurance firms vie for a growth position in embedded financial services. Matt Harris of Bain Capital Ventures’ prediction of embedded fintech suggests a proliferation of cloud-based Fintech apps around the core: payments, lending, insurance. That creates an ideal situation for AI-related alliances and partnerships among the incumbent lenders, startups, data aggregators and the CRAs. To Harris, the layers of the stack are centered around connectivity, intelligence, and ubiquity. According to Crunchbase, there have been 51 Fintech acquisitions in 2019 alone. Plaid’s acquisition of Quovo in January for approximately $200 million and Fiserv’s acquisition of First Data reflect how Fintechs are creating their own unique tech stacks already.
  8.  Zest predicts Fintechs will seek out AI and ML modeling expertise more so than build expertise and teams on their own, which will be costlier and take longer. Embedded Fintech’s future adoption rate is predicated on how effective development efforts are today at minimizing incidental bias and providing customers with greater visibility into how and why models provide specific results “Some of these startups are bringing their own data science and ML models. We have to hope these firms own, build, or buy the tools to ensure their models are inclusive, free of incidental bias, and use transparent AI customers can trust. We see explainable AI as being an essential feature or service in that tech stack,” says Zest’s Silverstein.
  9.  Fintechs will rely on AI and ML to help close the talent gap each of them has today while also improving the effectiveness of their talent management strategies. Finding, recruiting, and hiring the best candidates for development, engineering, marketing, sales, and senior management roles is an area Fintechs will increasingly adopt AI and ML for in 2020. Fintech CEOs and CHROs will begin upskilling programs for themselves and their teams to increase AI fluency and skills mastery in 2020. According to a recent Harris Interactive survey completed in collaboration with Eightfold titled Talent Intelligence And Management Report 2019-2020, 73% of U.S. CEOs and CHROs plan to use more AI in the next three years to improve talent management.
  10. Credit unions will adopt ML in 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services, including improvements in inquiry resolution & dispute and fraud management. Credit unions are built on an annuity-based business model that delivers successively higher profitability the longer a member is retained. Credit unions will capitalize on ML by driving up loan approvals with no added risk and automating more of the loan approval process. By the end of 2020, according to a Fannie Mae survey of mortgage lenders, 71% of credit unions plan to investigate, test, or fully implement AI/ML solutions – up from just 40% in 2018. AI and ML will also be adopted across credit unions to improve inquiry resolution & dispute and fraud management while improving multichannel customer experiences. Providing real-time, relevant responses to customers to expedite inquiries and dispute resolutions using AI and ML is going to become commonplace in 2020. AI and ML are predicted to make a significant contribution to automating anomaly detection and borrower default risk assessment as the graphic below from Fannie Mae’s Mortgage Lender Sentiment Survey® How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis illustrates:

 

 

Predicting How AI Will Improve Talent Management In 2020

Predicting How AI Will Improve Talent Management In 2020

47% of U.S.-based enterprises are using AI today for recruitment, leading all countries in the survey. U.S.-based enterprises’’ adoption of AI for recruitment soared in the last year, jumping from 22% in 2018 to 47% this year based on last years’ Harris Interactive Talent Intelligence and Management Report 2018.

  • 73% of U.S. CEOs and CHROs plan to use more AI in the next three years to improve talent management.
  • U.S.-based enterprises’’ adoption of AI for recruitment soared in the last year, jumping from 22% in 2018 to 47% this year.
  • U.S.-based enterprises lead in the use of AI to automate repetitive tasks (44%) and employee retention (42%).

These and many other fascinating insights are from a recent study completed by Harris Interactive in collaboration with Eightfold titled Talent Intelligence And Management Report 2019-2020, which provides insights into how CHROs are adopting AI today and in the future. You can download a copy here. A total of 1,350 CEOs and CHROs from the U.S., France, Germany, and the U.K. responded to the survey. One of the most noteworthy findings is how U.S-based CEOs and CHROs lead the world in prioritizing and taking action on improving their teams and their own AI skills. The more expertise they and their teams have with AI, the more effective they will be achieving operational improvements while taming the bias beast. The following graphic provides insights into how the four nations surveyed vary by their CEOs’ and CHROs’ perception of new technologies having had positive impacts, their plans for using AI in three years, and employee’s concerns about AI:

Predicting How AI Will Improve Talent Management In 2020

Predicting The Future Of AI In Talent Management

Four leading experts who are actively advising clients, implementing, and using AI to solve talent management challenges shared their predictions of how AI will improve talent management in 2020. The panel includes Kelly O. Kay, Partner, Heidrick & Struggles, Jared Lucas, Chief People Officer at MobileIron, Mandy Sebel, Senior Vice President, People at UiPath and David Windley CEO, IQTalent Partners. Mr. Kay leads the Software Practice for Heidrick & Struggles, a leading executive search and consulting firm commented: “As we all know, the talent crisis of 2019 is real and Eightfold’s application of AI on today is the most impactful approach I’ve seen and the outcomes they deliver eliminate unconscious bias, increases transparency and improves matching supply and demand of talent.” The following are their predictions of how AI will improve the following areas of talent management in 2020:

  • “Pertaining to talent attraction & acquisition-as adoption of intelligent automation and AI tools increases hiring managers and recruiters more easily uncover and surface overlooked talent pools,” said Mandy Sebel, Senior Vice President, People at UiPath.
  • “I predict that AI will become a requirement for companies in the screening of candidates due to the pervasive need to find higher-quality candidates at a faster pace,” said Jared Lucas, Chief People Officer at MobileIron.
  • “I believe the use of AI in the talent acquisition space will begin to hit critical mass in 2020. We are still in the early adopter phase, but the use of AI to match potential candidates to job profiles is catching on. Especially the use of AI for rediscovering candidates in ATS systems of larger corporations. Companies like Eightfold, Hiretual, and Atipica are leading the way,” said David Windley CEO, IQTalent Partners.
  • “Fear of job replacement will also subside, and more focus on job/role evolution as teams are experiencing firsthand how respective task elimination allows them to do more meaningful work,” commented Mandy Sebel, Senior Vice President, People at UiPath.
  • AI will provide the insights needed for CHROs to retain and grow their best talent, according to Jared Lucas, Chief People Officer at MobileIron. “I predict that AI will drive better internal mobility and internal candidate identification as companies are better able to mine their internal talent to fill critical roles,” he said.
  • Having gained credibility for executive and senior management recruiting, AI platforms’ use will continue to proliferate in 2020. “Private Equity is beginning to commercialize how AI can help select executives for roles based on competencies and experiences, which is exciting!” said Kelly O. Kay, Partner, Heidrick & Struggles.

Top 25 AI Startups Who Raised The Most Money In 2019

Top 25 AI Startups Who Raised The Most Money In 2019

  • $10.7B was invested in AI startups this year in their seed, early-stage venture, or late-stage venture funding rounds.
  •  Over half, or 57.9% of all AI startup financing rounds where either seed or pre-seed, 21.2% are Series A, 11.8% are Series B, and all others comprise 9% of all funding rounds.
  • The median AI startup funding round generated $4M with the average being $14.6M and the maximum, $319M, obtained by Vacasa.

These and many other fascinating insights are from an analysis of AI startups’ funding rounds in 2019 using Crunchbase Pro research. AI startups who have had seed, early-stage venture or late-stage venture funding since December 31, 2018, and are U.S.-based are included in the analysis which is provided here. Crunchbase Pro found 499 startups meeting the search criteria as of today.

Top 25 AI Startups Who Have Raised The Most Money In 2019

  1. Vacasa – Raised $319M from a Series C round on October 29th, Vacasa is creating and using AI-driven tools to improve their customers’ experiences renting vacation homes around the world. Their AI strategies include improving every aspect of the customer’s lifecycle from pricing through scheduling post-stay cleans. The company manages a growing portfolio of more than 14,000 vacation homes in the U.S, Europe, Central, and South America, and South Africa.
  2. Samsara – Raised $300M from a Series F round on September 10th. Samsara is an IoT platform combining hardware, software, and cloud to bring real-time visibility, analytics, and AI to operations. Samsara’s portfolio of Internet of Things (IoT) solutions combine hardware, software, and cloud to bring real-time visibility, analytics, and AI to operations. Their core strengths include vehicle telematics, driver safety, mobile workflow and compliance, asset tracking, and industrial process controls all in an integrated, open, real-time platform.
  3. TripActions – Raised $250M from a Series D round on June 27th. TripActions is a business travel platform that combines the latest AI-driven personalization with inventory and 24×7 365 live human support to serve employees, finance leaders, and travel managers alike all while empowering organizations to seize travel as a strategic lever for growth.
  4. ThoughtSpot – Raised $248M from a Series E round on August 22nd. ThoughtSpot’s AI-Driven analytics platform enables business analyst to capitalize on the expertise and shared knowledge of experienced data scientists. With ThoughtSpot, business analysts can analyze data or automatically get trusted insights pushed to you with a single click. ThoughtSpot connects with any on-premise, cloud, big data, or desktop data source. Business Intelligence and Analytics teams have used ThoughtSpot to cut reporting backlogs by more than 90% and make more than 3 million decisions and counting.
  5. CloudMinds – Raised $186M from a Series B round on February 23rd. Founded in 2015, CloudMinds’ unique Cloud Robot Service Platform consists of Human Augmented Robotics Intelligence with Extreme Reality (HARIX), a Secure virtual backbone network (VBN over 4G/5G), and Robot Control Unit (RCU). Designed by CloudMinds, XR-1 Robot is the first commercial humanoid service robot powered by our Smart Compliant Actuator (SCA) technology with precise and compliant grasping capability. Their AI Cloud Brain platform (HARIX) is designed to enable robotic intelligence through a secured network over 4G/5G. CloudMinds is focused on several core technologies, including Smart Vision, Smart Voice, Smart Motion and Human Augmentation. The following is an overview of their architecture:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Icertis – Raised $115M from a Series E round on July 17th. Icertis is an enterprise contract management platform in the cloud that solves contract management problems using AI. Using advanced algorithms, Icertis helps its customers accelerate business cycles by increasing contract velocity, protecting against risk by ensuring regulatory and policy compliance and optimizing the commercial relationships by maximizing revenue and reducing costs. 3M, Airbus, Cognizant, Daimler, Microsoft, and Roche who rely on Icertis to manage 5.7 million contracts in 40+ languages across 90+ countries, are all customers. The following is an overview of the Icertis Contract Management Platform:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. SparkCognition – Raised $100M from a Series C round on October 8th. SparkCognition builds artificial intelligence systems focused on the needs of its customers in the aviation, cybersecurity, defense, Financial Services, manufacturing, maritime, and Utilities industries. SparkCognition offers four main products: DarwinTM, DeepArmor, SparkPredict, and DeepNLPTM. One of their most noteworthy products is DeepArmor, an AI-powered endpoint security solution that has trained on millions of malicious and benign files and provides industry-leading protection against a broad spectrum of threats. With millions of new malware variants showing up each month, DeepArmor uses AI to assess risk levels and thwart malware and break attempts. DeepArmor’s dashboard is shown below:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Vectra AI – Raised $100M from a Series E round on June 10th. Vectra specializes in network detection and response – from cloud and data center workloads to user and IoT devices. Its Cognito platform accelerates threat detection and investigation using artificial intelligence to collect, store, and enrich network metadata with the right context to detect, hunt and investigate known and unknown threats in real-time.
  2. Globality – Raised $100M from a Series D round on January 22nd. The January round enabled Globality to accelerate its growth through investment in its AI technology, increasing business capacity by hiring additional members of its engineering, product, and client teams, and expanding its Marketing and Sales programs. Through its AI-powered Platform, Globality is automating the procurement of B2B services and improving the RFP process. Globality efficiently matches companies with service providers that meet their specific needs, cutting the sourcing process from months to hours, and delivering savings of 20% or more for companies.
  3. Black Sesame Technologies – Raised $100M from a Series B round on April 12th.  Black Sesame Technologies is an AI digital imaging technology firm provides solutions for image processing and computing images, as well as embedded sensing platforms. The firm specializes in algorithms for smartphones, autonomous driving, and other consumer electronics. Its R & D teams are actively working on core algorithm development, ASIC design, software system, and ADAS engineering applications.
  4. Scale – Raised $100M from a Series C round on August 5th. Scale accelerates the development of AI applications by helping computer vision teams generate high-quality ground truth data. Our advanced LiDAR, video, and image annotation APIs allow self-driving, drone, and robotics teams at companies like Waymo, OpenAI, Lyft, Zoox, Pinterest, and Airbnb focus on building differentiated models vs. labeling data. Scale’s greatest strength is its API for training data, providing access to human-powered data for a multitude of use cases.
  5. AutoX – Raised $100M from a Series A round on September 16th. AutoX is a self-driving car startup that uses AI to fine-tune Location-Based Services with camera-first autonomous driving technology. In July of this year, AutoX announced a partnership with NEVS, the Swedish holding company, and electric vehicle manufacturer that bought Saab’s assets out of bankruptcy, to deploy a robotaxi pilot service in Europe by the end of 2020.
  6. DISCO – Raised $83M from a Series E round on January 24th. DISCO is a legal technology company that applies artificial intelligence and cloud computing to legal problems to help lawyers and legal teams improve legal outcomes for clients. Corporate legal departments, law firms, and government agencies around the world use DISCO as an ediscovery solution for compliance, disputes, and investigations. The company is looking to reinvent legal technology to automate and simplify complex and error-prone tasks that distract from practicing law.
  7. QOMPLX – Raised $78.6M from a Series A round on July 23rd. QOMPLX makes it faster and easier for organizations to integrate disparate internal and external data sources across the enterprise via a unified analytics infrastructure that supports better decision-making using AI at scale. This enterprise data-fabric is called QOMPLX OS: an enterprise operating system that powers QOMPLX’s decision platforms in cybersecurity, insurance, and quantitative finance. The following is an example of how the QOMPLX OS automates data management while providing greater contextual intelligence to data:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Galileo Financial Technologies – Raised $77M from a Series A round on October 17th. Galileo’s APIs are used widely throughout the neobank, payments, gig economy, investing and SaaS market segments. As of September 2019, Galileo was managing over $26B in annual payments volume, a 130% increase over September 2018. Galileo’s latest round, a $77M investment led by venture capital firm Accel with participation from Qualtrics Co-Founder & CEO Ryan Smith. The company, which is already profitable and growing rapidly, plans to use the funds to accelerate growth, including expansion into Latin America, the UK, and Europe, and for continued product expansion.
  2. BlackThorn Therapeutics – Raised 76M from a Series B round on June 13th. BlackThorn Therapeutics, Inc., is a clinical-stage neurobehavioral health company pioneering the next generation of AI technologies to advance its pipeline of targeted therapeutics for treating brain disorders. The company has engineered PathFinder, a cloud-based computational psychiatry and data platform, to enable the collection, integration, and analysis of multimodal data at great speed and scale. BlackThorn applies its data-driven approaches to create an understanding of the core underlying pathophysiology of neurobehavioral disorders and uses these insights to generate objective neuromarkers, which support drug target identification, patient stratification, and objective clinical trial endpoints.
  3. Highspot – Raised $75M from a Series D round on December 3rd. Highspot is a sales enablement platform that relies on AI technologies to elevate and add value to companies’ conversations with their customers and drive strategic growth. The platform combines intelligent content management, training, contextual guidance, customer engagement, and actionable analytics. Revenue teams use Highspot to deliver a unified buying experience that increases revenue, customer satisfaction and retention. Highspot has attained a 90% average monthly recurring usage rate and has global support across 125 countries. It’s available on the Salesforce AppExchange, Microsoft Store, Google Play and Apple AppStore.
  4. Moveworks – Raised $75M from a Series B round on November 11th. Moveworks is a cloud-based AI platform designed for large enterprises’ IT support and service desk challenges. Instead of just tracking issues, Moveworks uses advanced AI to solve IT support and service problems automatically, often with no human intervention. Customers include AutoDesk, Broadcom, Nutanix and many other Fortune 500 companies. Moveworks is backed by Bain Capital Ventures and Lightspeed Venture Partners and is headquartered in Mountain View, California.
  5. Reonomy – Raised $60M from a Series D round on November 7th. Reonomy is an AI-powered data platform for the commercial real estate industry. The goal of the company’s platform is to leverage big data, partnerships, and machine learning to connect the fragmented world of commercial real estate. Reonomy products enable individuals, teams, and companies to unlock new insights from property intelligence. By constantly aggregating and organizing up-to-the-minute marketplace data, Reonomy offer investors and brokers the opportunity to research nuanced property characteristics that indicate the likelihood of a future sale. Below is an example of an analysis of the San Francisco neighborhood using AI-based filtering technology:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Clari – Raised $60M from a Series D round on October 10th. Clari is a connected revenue operations platform that uses automation and AI to unlock all the activity data captured in key business systems such as marketing automation, CRM, email, calendar, phone, content management, and conversations. It automatically aligns that data to accounts and opportunities to deliver visibility, forecasting, and apply predictive insights, which results in more insight, less guesswork, and more predictable revenue. Clari helps companies by changing their revenue operations to be more connected, efficient, and predictable. Clari’s platform is used by hundreds of sales, marketing, and customer success teams at B2B companies such as Qualtrics, Lenovo, Adobe, Dropbox, and Okta to control pipeline, audit deals and accounts, forecast the business, and reduce churn. The following is an example of a Clari dashboard:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. People.ai – Raised $60M from a Series C round on May 21st. People.ai is an artificial intelligence (AI) platform for enterprise revenue. People.ai helps sales, marketing, and customer success teams uncover every revenue opportunity from every customer by capturing all customer contacts, activity, and engagement to drive actionable insights across all revenue teams. People.ai enables sales leaders to be more effective at managing their teams and growing revenue by giving them a complete picture of sales activities and leveraging AI to deliver sales performance analytics, personalized coaching, one-on-one feedback, and pipeline reviews. The People.ai platform identifies and targets the buying group, and gives marketers a clear visualization of whom sales have spoken with, and which campaign has been successful in each opportunity. Using this information, marketers are able to build personas and deal models in order to better target their marketing efforts and get better campaign ROI. Customer success and services teams use People.ai to ensure they are engaging with the right people when the customer is handed off to them, but more importantly, these post-sales teams are constantly looking to align their effort and activities with the right opportunities and customers, tracking the true cost to support each customer. The following graphic illustrates the People.ai platform automatically capture all contact and customer activity data, dynamically update your CRM, and provide actionable intelligence to realize the full potential of customer-facing teams. The following graphic illustrates the People.ai platform:

Top 25 AI Startups Who Raised The Most Money In 2019

 

  1. Invoca – Raised $56M from a Series C round on October 17th. Invoca is an AI-powered call tracking and analytics platform that helps marketers drive inbound calls and turn them into sales. The platform delivers real-time call analytics to help marketers take informed actions based on data generated before and during a phone conversation. It also allows marketers to understand, in real-time, the factors affecting consumers’ intent to buy, like competitive promotional campaigns. Marketers can put the data to work directly in the platform by automating customer experience workflows during, before, and after each call. Invoca’s platform integrates with Google Marketing Platform, Facebook, Adobe Experience Cloud, and Salesforce Sales and Marketing Clouds. Invoca’s investors include Accel Partners, H.I.G. Growth Partners, Upfront Ventures, Morgan Stanley Alternative Investment Partners, Salesforce Ventures, and Rincon Venture Partners. The following is an example of an Invoca dashboard used for measuring Google AdWords effectiveness:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Clinc – Raised $52M from a Series B round on May 20th.  Clinc is a conversational AI platform that enables enterprises to build “human-in-the-room” level, next-gen, virtual assistants. In contrast to a speech-to-text word matching algorithm, Clinc analyzes dozens of factors from the user’s input including wording, sentiment, intent, tone of voice, time of day, location, and relationships, and uses those factors to deliver an answer that represents a composite of knowledge extracted from its trained brain. Clinc’s underlying technology is based on state-of-the-art machine learning and deep neural networks (DNN)-as-a-service developed by computer science professors at the University of Michigan. Clinc is a standalone “trained brain” that has been given an initial deep knowledge of the financial and banking industry. Its machine learning capabilities enable it to expand its knowledge with every query and to then draw from that knowledge for each subsequent customer query.
  2. Biz2Credit – Raised $52M from a Series D round on June 4th. Biz2Credit is a hub connecting small business owners with lenders and service providers, and seek solutions based on their online profiles. Biz2X uses a streamlined user interface, AI-driven analytics, and a customizable white label environment to help banks enhance their core services such as offering focused customer service, growing their portfolio, and increasing the use of their products. With enhanced loan management, servicing, risk analytics and a configurable customer journey, Biz2X is helping banks like these run their lending operations at scale.
  3. Uniphore – Raised $51M from a Series C round on August 13th. Uniphore is a global Conversational AI technology company that offers a customer service platform that is powered by AI and automation technologies. The Company’s vision is to bridge the gap between people and machines through voice. Uniphore enables businesses globally to deliver transformational customer service by providing a platform of Conversational Analytics, Conversational Assistant, and Conversational Security that changes the way enterprises engage their consumers, build loyalty and realize efficiencies.

 

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