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Posts from the ‘Machine learning’ Category

How To Know If An E-Mail Is Trustworthy

How To Know If An E-Mail Is Trustworthy

 

Bottom Line: Phishing is the leading cause of all breaches, succeeding because impersonation, redirection, and social engineering methods are always improving. And, phishing is only one way e-mails are used in fraud. Businesses need to understand if an e-mail address can be trusted before moving forward with a transaction.

Microsoft thwarts billions of phishing attempts a year on Office365 alone by relying on heuristics, detonation, and machine learning, strengthened by Microsoft Threat Protection Services. In 2018 Microsoft blocked 5 billion phish e-mails in Office 365 and detonated 11 billion unique items by ATP sandboxing. Microsoft is succeeding with its cybersecurity partners in defeating phishing attacks. Phishers are going to extraordinary lengths to discover new techniques to evade detection and successfully carry out phishing attempts. By analyzing Office 365 ATP signals, Microsoft sees phishers attempt to abuse many legitimate cloud services, including Amazon, Google, Microsoft Office365, Microsoft Azure, and others. Microsoft is creating processes that identify and destroy phishing attempts without impacting legitimate applications’ performance.

Phishers’ Favorite Trojan Horse Is Office365 Followed By Cybersecurity Companies  

Phishers are hiding malicious links, scripts and, in some cases, mutated software code behind legitimate Microsoft files and code to evade detection. Using legitimate code and links as a Trojan Horse to successfully launch a phishing campaign became very popular in 2019 and continues today. Cybercriminals and state-sponsored hackers have been mutating legitimate code and applications for years attempting to exfiltrate priceless data from enterprises and governments globally. Office365 is the phisher’s Trojan Horse of choice, closely followed dozens of cybersecurity companies that have seen hackers attempt to impersonate their products. Cybersecurity companies targeted include Citrix, Comodo, Imperva, Kaspersky, LastPass, Microsoft, BitDefender, CyberRoam, and others.

Using Trojan Horses To Hijack Search Results

In 2019 Microsoft discovered a sophisticated phishing attack that combined impersonation, redirection, and social engineering methods. The phishing attack relied on using links to Google search results as a Trojan Horse to deliver URLs that were poisoned so that they pointed to an attacker-controlled page, which eventually redirected to the phishing page. Microsoft discovered that a traffic generator ensured that the redirector page was the top result for specific keywords. The following graphic explains how the phishing attack was used to poison search results:

Using this workflow, phishers attempted to send phishing e-mails that relied on legitimate URLs as their Trojan Horses from legitimate domains to take advantage of the recipient’s trust. Knowing which e-mails to trust or not is becoming foundational to stopping fraud and phishing attacks.

How Kount Is Battling Sophisticated Attacks 

Meanwhile, e-mail addresses can be a valuable source of information for businesses looking to prevent digital fraud. Misplaced trust can lead to chargebacks, manual reviews, and other undesirable outcomes. But, Kount’s Real-Time Identity Trust Network calculates Identity Trust Levels in milliseconds, reducing friction, blocking fraud, and delivering improved user experiences. Kount discovered that e-mail age is one of the most reliable identity trust signals there are for identifying and stopping automated fraudulent activity.

Based on their research and product development, Kount announced Email First Seen capabilities as part of its AI-powered Identity Trust Global Network. Email First Seen applies throughout the customer journey, from payments to account login to account creation. The Identity Trust Global Network consists of fraud and trust signals from over half a billion e-mail addresses. It also spans 32 billion annual interactions and 17.5 billion devices across 75 business sectors and 50-plus payment providers and card networks. The network is linked by Kount’s next-generation artificial intelligence (AI) and works to establish real-time trust for each identity behind a payment transaction, log in or account creation

E-mail Age Is Proving To Be A Reliable Indicator Of Trust

A favorite tactic of cybercriminals is to create as many new e-mail aliases as they need to deceive online businesses and defraud them of merchandise and payments. Kount is finding that when businesses can identify the age of an e-mail address, they can more accurately determine identity trust. Kount’s expertise is in fraud prevention effectiveness, relying on a combination of fraud and risk signals to generate a complete picture of authentication details. The following graphic illustrates what a Kount customer using Email First Seen will see in every e-mail they receive.

Kount’s Identity Trust Global Network relies on AI-based algorithms that can analyze all available identifiers or data points to establish real-time links between identity elements, and return identity trust decisions in real-time. Kount’s unique approach to using AI to improve customer experiences by reducing friction while blocking fraud reflects the future of fraud detection. Also, Kount’s AI can discern if additional authentication is needed to verify the identity behind the transaction and relies on half a billion e-mail addresses that are integral to AI-based analysis and risk scoring algorithms. Kount is making Email First Seen available to all existing customers for no charge. It’s been designed to be native on the Kount platform, allowing the information to be accessible in real-time to inform fraud and trust decisions.

Conclusion

In 2020 phishing attempts will increasingly rely on legitimate code, links, and executables as Trojan Horses to evade detection and launch phishing attacks at specific targets. Microsoft’s research and continued monitoring of phishing attempts uncovered architecturally sophisticated approaches to misdirecting victims through impersonation and social engineering.

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.

Five Factors Predicting The Future Of MacOS Management And Security

Bottom Line: Going into 2020, CISOs’ sense of urgency for managing their fleets of Android, Apple iOS & macOS, Windows Phone, and Windows 10 devices all from an integrated Unified Endpoint Management (UEM) is transforming the MacOS Management and Security landscape.

For many, CISOs, the highest priority project they’re starting the New Year with is getting their diverse fleet of devices on a common unified endpoint management platform. “We’ve gone through no less than a dozen UEMs (Unified Endpoint Management) systems, and they are either very good at supporting iOS and macOS or terrible at every other operating system or vice versa,” the CISO of a leading insurance and financial services firm told me over lunch recently. “Our sales, marketing, graphic artists, DevOps, and Customer Success teams all are running on Macs and iPhones, which makes it even more of a challenge to get everyone on the same endpoint management platform.” He went on to explain that the majority of macOS and iOS endpoint management systems aren’t built to support the advanced security he needs for protecting Android, Windows Phone, and Windows 10 devices.

Unified Endpoint Management is a key CISO priority in 2020

macOS and iOS devices had their own endpoint management tools in previous years when they were limited in use. Now they’re common in the enterprise and need to be considered part of an organization-wide fleet of devices, making it a high priority to add them to the unified endpoint management platform all other devices are on. Further accelerating this change is the success of BYOD policies that give employees the choice of using the tablets, smartphones, and laptops they’re the most productive with. One CISO told me their BYOD program made it clear macOS and iOS are the de facto standard across their enterprise.

While endpoint management platforms are going through an Apple-driven inflection point, forcing the need for a more inclusive unified endpoint management strategy, CISOs are focusing on how to improve application and content control at the same time. How enterprises choose to solve that challenge are predicting the future of MacOS management and security.

Five Factors Driving the Future of macOS Management and Security

CISOs piloting and only buying platforms that can equally protect every device operating system, macOS, and iOS’ rapidly growing enterprise popularity and better support for adaptive access are a few of the catalysts redefining the landscape today. The following five factors are defining how MacOS Management and Security will improve in 2020:

  • Enterprises need more effective endpoint and application management that includes Android, Apple iOS & macOS, Windows Phone, and Windows 10. There’s a major gap in how effective endpoint protection is across the UEM platforms today. Data-at-risk encryption and App distribution, or how well a UEM system can create, update, and distribute macOS applications are two areas cybersecurity teams are focusing on today.

Five Factors Predicting The Future Of MacOS Management And Security

  • System integration options needs to extend beyond log reports and provide real-time links to Security Information and Event Management (SIEM) systems. CISOs and their cybersecurity teams need real-time integration to incident management systems so they can be more effective troubleshooting potential breach attempts. Sharing log files across other systems is a first step, yet real-time integration is clearly what’s needed to protect enterprises’ many devices and threat surfaces today. The following Splunk dashboard illustrates the benefits of having real-time integration beyond log reports, encompassing SIEM systems:

Five Factors Predicting The Future Of MacOS Management And Security

  • UEM platforms that differentiate between corporate-owned and personal devices, content and authentication workflows, and data are defining the future of macOS Management and Security. Key factors that CISOs need in this area of unmanaged device support include more effective content separation, improved privacy settings, support for actions taken on personally-owned devices, and role-based privacy settings. MobileIron is a leader in this area, with enterprises currently using their role-based workflows to limit and verify access to employee-owned devices. MobileIron can also limit IT’s scope of control over an employee device, including turning off location tracking.
  • Support and proven integration of Identity solutions such as Okta, Ping Identity, Microsoft, and Single sign-on (SSO) are defining the future of adaptive access today. This is the most nascent area of UEM platform development today, yet the one area that CISOs need the greatest progress on this year. Endpoint protection and system integration are the two areas that most define how advanced a given UEM providers’ platform is today.
  • The ability to provision, revoke, and manage device certificates over their lifecycles is becoming a must-have in enterprises today. UEM platforms, in large part, can handle certificate device provisioning, yet Certificate Authority (CA) integration is an area many struggle with. CISOs are asking for more effective certificate lifecycle management, especially given the proliferation of macOS and iOS devices.

Conclusion

The five factors of MacOS management and security are transforming the Unified Endpoint Management (UEM) solution landscape. CISOs often speak of wanting to have a more integrated UEM strategy, one that can provide better SIEM system integration, differentiate between corporate-owned and personal devices, and also manage the lifecycles of device certificates. MobileIron has proven their ability to scale in a BYOD world and is a UEM vendor to watch in 2020.

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

Bottom Line: By securing every endpoint with a persistent connection and the resiliency to autonomously self-heal, CIOs are finding new ways to further improve network security by capitalizing on each IT assets’ intelligence.

Capturing real-time data from IT assets is how every organization can grow beyond its existing boundaries with greater security, speed, and trust. Many IT and cybersecurity teams and the CIOs that lead them, and with whom I’ve spoken with, are energized by the opportunity to create secured perimeterless networks that can flex in real-time as their businesses grow. Having a persistent connection to every device across an organizations’ constantly changing perimeter provides invaluable data for achieving this goal. The real-time data provided by persistent device connections give IT and cybersecurity teams the Asset Intelligence they need for creating more resilient, self-healing endpoints as well.

How Asset Intelligence Drives Stronger Endpoint Security 

Real-time, persistent connections to every device in a network is the foundation of a strong endpoint security strategy. It’s also essential for controlling device operating expenses (OPEX) across the broad base of device use cases every organization relies on to succeed. Long-term persistent connections drive down capital expenses (CAPEX) too, by extending the life of every device while providing perimeterless growth of the network. By combining device inventory and analysis, endpoint data compliance with the ability to manage a device fleet using universal asset management techniques, IT and cybersecurity teams are moving beyond Asset Management to Asset Intelligence. Advanced analytics, benchmarks, and audits are all possible across every endpoint today. The following are the 10 ways Asset Intelligence improves cybersecurity resiliency and persistence:

  • Track, trace and find lost or stolen devices on or off an organizations’ network in real-time, disabling the device if necessary. Every device, from laptops, tablets, and smartphones to desktops and specialized use devices are another threat surface that needs to be protected. Real-time persistent connections to each of these devices make track-and-trace possible, giving CIOs and their teams more control than had been possible before. Real-time track-and-trace data combined with device condition feedback closes security blind spots too. IT and cybersecurity teams can monitor every device and know the state of hardware, software, network and use patterns from dashboards. Of the endpoint providers in this market, Absolute’s approach to providing dashboards that provide real-time visibility and control of every device on a network is considered state-of-the-art. An example of Absolute’s dashboard is shown below:

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

  • Asset Intelligence enables every endpoint to autonomously self-heal themselves and deliver constant persistence across an organization’s entire network. By capitalizing on the device, network, threat, and use data that defines Asset Intelligence, endpoint agents learn over time how to withstand breach attempts, user errors, and malicious attacks, and most importantly, how to return an endpoint device to its original safe state. Asset Intelligence is the future of endpoint security as it’s proving to be very effective at enabling self-healing persistence across enterprise networks.
  • Asset Intelligence solves the urgent problem created from having 10 or more agents installed on a single endpoint that collide, conflict and decay how secure the endpoint is. Absolute Software’s 2019 Endpoint Security Trends Report found that the more agents that are added to an endpoint, the greater the risk of a breach. Absolute also found that a typical device has ten or more endpoint security agents installed, often colliding and conflicting with the other. MITRE’s Cybersecurity research practice found there are on average, ten security agents on each device, and over 5,000 common vulnerabilities and exposures (CVEs) found on the top 20 client applications in 2018 alone.
  • Asset Intelligence sets the data foundation for achieving always-on persistence by tracking every devices’ unique attributes, identifiers, communication log history and more. Endpoint security platforms need a contextually-rich, real-time stream of data to know how and when to initialize the process of autonomously healing a given endpoint device. Asset Intelligence provides the centralized base of IT security controls needed for making endpoint persistence possible.
  • Having a real-time connection to every device on a perimeterless network contributes to creating a security cloud stack from the BIOS level that delivers persistence for every device. CIOs and CISOs interested in building secured perimeterless networks are focused on creating persistent, real-time connections to every device as a first step to creating a security cloud stack from each devices’ BIOS level. They’re saying that the greater the level of Asset Intelligence they can achieve, the broader they can roll out persistence-based endpoints across their networks that have the capacity to self-diagnose and self-heal.
  • Device fleets are churning 20% a year or more, increasing the urgency CIOs have for knowing where each device is and its current state, further underscoring Asset Intelligence’s value. Gavin Cockburn of ARUP is the global service lead for workplace automation and endpoint management, including how the firm acquires devices, manages and reclaims them. ARUP is using the Absolute Persistence platform for managing the many high-value laptops and remote devices their associates use on global projects. During a recent panel discussion he says that device replacements “becomes part of our budgeting process in that 33% of devices that we do replace every year, we know where they are.” Gavin is also using API calls to gain analytical data to measure how devices are being used, if the hard drive is encrypted or not and run Reach scripts to better encrypt a device if there is not enough security on them.
  • The more Asset Intelligence an organization has, the more they can predict and detect malware intrusion attempts, block them and restore any damage to any device on their perimeter. When there’s persistent endpoint protection across a perimeterless network, real-time data is enabling greater levels of Asset Intelligence which is invaluable in identifying, blocking and learning from malware attempts on any device on the network. Endpoint protection platforms that have persistence designed in are able to autonomously self-heal back to their original state after an attack, all without manual intervention.
  • Persistent endpoints open up the opportunity of defining geofencing for every device on a perimeterless network, further providing valuable data Asset Intelligence platforms capitalize on. Geofencing is proving to be a must-have for many organizations that have globally-based operations, as their IT and cybersecurity teams need to track the device location, usage, and compliance in real-time. Healthcare companies are especially focused on how Asset Intelligence can deliver geofencing at scale. Janet Hunt, Senior Director, IT User Support at Apria Healthcare recently commented during a recent panel discussion that “our geo-fencing is extremely tight. I have PCs that live in the Philippines. I have PCs that live in India. I have one PC or actually two PCs that live in Indonesia. If somebody goes from where they say that they’re going to be to another part of Indonesia, that device will freeze because that’s not where it’s supposed to be and that’s an automatic thing. Don’t ask forgiveness, don’t ask questions, freeze the device and see what happens. It’s one of the best things we’ve done for ourselves.”  Gavin Cockburn says, “We actually do some kind of secretive work, government work and we have these secure rooms, dotted around the organization. So we know if we put a device in that room, what we do is, what we say is this device only works in this area and we can pinpoint that to a pretty decent accuracy.”  From healthcare to secured government contracting, geofencing is a must-have in any persistent endpoint security strategy.
  • Automating customer and regulatory audits and improving compliance reporting by relying on Asset Intelligence alleviates time-consuming tasks for IT and cybersecurity teams. When persistent endpoint protection is operating across an organization’s network, audit and compliance data is captured in real-time and automatically fed into reporting systems and dashboards. CIOs and their cybersecurity teams are using dashboards to monitor every device’s usage patterns, audit access, and application activity, and check for compliance to security and reporting standards. Audits and compliance reporting are being automated today using PowerShell, BASH scripts and API-based universal asset commands. Gavin Cockburn of ARUP mentioned how his firm gives customers the assurance their data is safe by providing them ongoing audits while project engagements are ongoing. “We need to show for our clients that we look after their data and we can prove that. And we show that again and again. I mean similar story, we’ve seen machines go missing, either breaking into cars, re-image three times. We wipe it every time. Put the new hard drive in, think it might be a hard drive issue, it wipes again. We never see it come online again, “ he said.
  • Asset Intelligence improves data hygiene, which has a direct effect on how effective all IT systems are and the customer experiences they deliver. CIOs and their teams’ incentives center on how effective IT is at meeting internal information needs that impact customer experiences and outcomes. Improving data hygiene is essential for IT to keep achieving their incentive plans and earning bonuses. As Janet Hunt, Senior Director, IT User Support at Apria Healthcare said, “right now we are all about hygiene and what I mean by that is we want our data to be good. We want all the things that make IT a valued partner with the business operation to be able to be reliable.” The more effective any organization is at achieving and sustaining a high level of data hygiene, the more secure their perimeterless network strategies become.

 

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

  • Duolingo, HOVER, Ironclad, Orbital Insight, People.ai, Dataiku, DeepMap, Cobalt, Aktana, Chorus.ai, Noodle Analytics, Inc. (Noodle.ai), Signal AI, Augury, SparkCognition, and KONUX are the most likely to be recommended by their employees to friends looking for a machine learning startup to work for in 2020.
  • 96% of the employees of the 15 highest rated machine learning startups would recommend their company to a friend looking for a new job, and 98% approve of their CEOs.
  • Across all machine learning startups with Glassdoor ratings, 74% of employees would recommend the startup they work for to a friend, and 81% approve of their CEO.
  • There are over 230 cities globally who have one or more machine learning startups in operation today with Crunchbase finding 144 in San Francisco, 60 in London, 69 in New York, 82 in Tel Aviv, 22 in Toronto, 20 in Paris, 18 in Seattle and the remainder distributed over 223 global locations.

These and many other insights are from a Crunchbase Pro analysis completed today using Glassdoor data to rank the best machine learning startups to work for in 2020. Demand reminds high for technical professionals with machine learning expertise.  According to Indeed, Machine Learning Engineer job openings grew 344% between 2015 to 2018 and have an average base salary of $146,085 according to their  Best Jobs In The U.S. Study. You can read the study shows that technical professionals with machine learning expertise are in an excellent position to bargain for the average base salary of at least $146,085 or more.

Methodology

In response to readers’ most common requests of which machine learning startups are the best to work for, a Crunchbase Pro query was created to find all machine learning startups who had received Seed, Early Stage Venture, or Late Stage Venture financing. The 2,682 machine learning startups Crunchbase is tracking were indexed by Total Funding Amount by startup to create a baseline.

Next, Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO were used to find the best startups to work for. 79 of the 150 machine learning startups have 15 or more Glassdoor reviews and are included in the analysis. 41 have less than 15 reviews and 30 have no reviews. The table below is a result of the analysis, and you can find the original Microsoft Excel data set here.

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

 

 

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:

 

 

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.

 

The Most Innovative Tech Companies Based On Patent Analytics

The Most Innovative Tech Companies Based On Patent Analytics

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  • Microsoft, Apple, and IBM lead the world in hardware & software patent innovation according to PatentSight.
  • Samsung, Johnson & Johnson, LG Electronics. Alphabet, Qualcomm, Ford, Intel, Microsoft, Sony, and VW are the ten most innovative companies in the world, according to PatentSight’s patent analytics research.
  • Ford leads the global automotive industry in patent innovation, due in large part to successful R&D efforts in autonomous driving.

These and many other fascinating insights are from Swiss consulting firm EconSight’s patent analytics research that first identified all the patents that are supposed to protect particularly relevant innovations – in this case, defined as innovations for the digitization of applied technologies – using the PatentSight database. Companies not only have to maintain their innovative strength; they also have to continue to expand in comparison to previous years to take a leading position in the ranking. For additional details on the methodology and to request the rank of your company, please visit the PatentSight Innovation Ranking 2019 site here.

Key insights from EconSight’s patent analytics research defining the most innovative companies globally include the following:

  • 38 of the most innovative companies in the world are based in the U.S, 21 in China, and 15 from Europe. Chinese followed by Japanese-based companies lead the world in electronics innovation as measured by the uniqueness of patents produced. U.S. companies lead the world in medical technology patent innovation. The following graphic compares the number of companies within the global top 100 ranking by country and industry for 2019.

  • In the U.S., tech companies dominate the top 10 most innovative companies in 2019. Alphabet, Qualcomm, Intel, Microsoft, Honeywell, Apple, and GE are producing the most unique, differentiated and value-adding patents based on EconSight’s methodology. Medical technology companies show the greatest growth in innovative patent production as the graphic below illustrates:

  • The world’s most innovative medical technology companies’ patent focus is on biosensors, surgical robotics, shortening the time-to-market for pharmaceutical drugs, and funding startup incubators that yield new patents. Johnson & Johnson’s (J&J) multifaceted innovation strategy reflects the broader strategic vision of every medical technology company pursuing new intellectual property (IP) that leads to patent leadership. J&J acquired Auris Health and Verb Surgical, which is managed as a joint venture with Alphabet’s Verily medical division, which gives them a patent portfolio in healthcare intelligence. J&J has in total acquired over 300 companies in the medical technology industry according to PatentSight’s analysis. These acquisitions have moved them into biosensors, surgical robotics, and startups performing drug research.

The Most Innovative Tech Companies Based On Patent Analytics

  • Japanese robotics manufacturer Fanuc is the world’s most innovative automation technology company based on patent analysis. Since 2019 Fanuc has jumped 42 places in the ranking, from 61st to 19th. The global labor shortage in manufacturing is a contributing factor to the strong market demand Fanuc is seeing for all its robotics products and systems. The following are the top 25 robotics companies of 2019:

The Most Innovative Tech Companies Based On Patent Analytics

  • Ford leads the global automotive industry in patent innovation, while Volkswagen doubles down on patents over the last two years. Ford leads the world in patent innovation due to its rapid advances in autonomous vehicle development. Volkswagen’s rapid ascent in the automotive industry rankings has made them the most innovative company in Germany based on patent analytics this year. VW is investing in autonomous vehicles, and the networking of mobility participants, setting a solid foundation for future vehicle models today.

The Most Innovative Tech Companies Based On Patent Analytics

PatentSight Background

PatentSight – A Lexis Nexis company– specializes in cleaning and refining patent data and providing advanced patent analytics. Publicly available patent data simply cannot be used without qualitative preparation and correction. Due to the sheer mass (about 3.3 million new registrations in 2018 alone), all available patents cannot be viewed manually. Publications in many different languages and often very abstract contents make a manual review and evaluation difficult not only for laymen but also for experts. A further challenge is to level out the widely differing citation practices of national patent offices or to document the legal status of patents.

PatentSight, through manually supervised and scientifically developed algorithms, has best-in-class information on ownership data, going far beyond the testing standards recommended by the World Intellectual Property Organization (WIPO).

Moreover, PatentSight‘s proprietary patent valuation metrics reveal which patents are key, and which are superfluous. Based on citations, global protection, and several correction factors, EconSight leveraged these metrics to determine the most innovative companies.

AI Skills Among The Most In-Demand For 2020

AI Skills Among The Most In-Demand For 2020

Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020.

  • TensorFlow is the most popular tech skill of the last three years, exponentially increasing between 2016 and 2019 based on Udemy’s
  • Udemy sees robust demand for AI and data science skills, in addition to web development frameworks, cloud computing, and IT certifications, including AWS, CompTIA & Docker.
  • SAP expertise is projected to be the fastest-growing process-related skill set in 2020.

These and many other fascinating insights are from Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future (48 pp., PDF, opt-in).  The report provides compelling evidence of how important it is to prepare workforces for the future of work in an AI-enabled world. Udemy predicts 2020 will be the year AI goes mainstream. The report states that “In the world of finance, investment funds managed by AI and computers account for 35% of America’s stock market today,” citing a recent article in The Economist, The rise of the financial machines. The following are the key insights from the report:

  • Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020. TensorFlow, OpenCV, and neural networks are the foundational skills many data scientists are pursuing and perfecting today to advance their AI-based career strategies. Mastering those three skills is essential for understanding and developing AI apps and platforms. TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. The following is a comparison of the top 10 most popular tech skills in 2020 and the top 10 tech skills that grew in popularity between 2016 and 2019.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

  • The top 10 emerging tech skills in 2020 will be web development, quantum computing, and Internet of Things IoT). Udemy analyzed the emerging skills that over 40M people are learning on Udemy today, and found that Gatsby.js, a new web development framework tool, is gaining rapid adoption. Additional web development tools include React Hooks, Next.js, and SwiftUI, a user interface tool for Apple apps. Entirely new skills, including quantum computing and ESP32, used in the IoT development, are also among the top 1 emerging tech skills of 2020.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

  • SAP enterprise software expertise, knowledge of the ISO/IEC 27001 standard, information security, and Microsoft Dynamics 365 are projected to be the four of the fastest-growing process and tools skills in 2020. Udemy also found a strong interest in Robotic Process Automation (RPA) and Business Process Management (BPM). Robotic Process Automation (RPA) refers to the use of process automation tools to quickly replicate how human beings perform routine daily office work using popular productivity apps, including Microsoft Excel, databases, or web applications.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

 

  • Chef Software expertise, network security, penetration testing, Linux security, and AWS Certified Cloud are predicted among the fastest-growing skills for IT professionals in 2020. Chef software is prevalent in IT organizations and is used for streamlining the task of configuring & maintaining a company’s servers. Chef has invested in integrating with many of the most popular cloud-based platforms, including Rackspace, Microsoft Azure, and Amazon Elastic Compute Cloud, to automatically provision and configure new machines.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

 

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