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Posts from the ‘Machine learning’ Category

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

 

10 Predictions How AI Will Improve Cybersecurity In 2020

10 Predictions How AI Will Improve Cybersecurity In 2020

Capgemini predicts 63% of organizations are planning to deploy AI in 2020 to improve cybersecurity, with the most popular application being network security.

Cybersecurity is at an inflection point entering 2020. Advances in AI and machine learning are accelerating its technological progress. Real-time data and analytics are making it possible to build stronger business cases, driving higher adoption. Cybersecurity spending has rarely been linked to increasing revenues or reducing costs, but that’s about to change in 2020.

What Leading Cybersecurity Experts Are Predicting For 2020

Interested in what the leading cybersecurity experts are thinking will happen in 2020, I contacted five of them. Experts I spoke with include Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software; Dr. Torsten George, Cybersecurity Evangelist at Centrify; Craig Sanderson, Vice President of Security Products at Infoblox; Josh Johnston, Director of AI, Kount; and Brian Foster, Senior Vice President Product Management at MobileIron. Each of them brings a knowledgeable, insightful, and unique perspective to how AI and machine learning will improve cybersecurity in 2020. The following are their ten predictions:

  1. AI and machine learning will continue to enable asset management improvements that also deliver exponential gains in IT security by providing greater endpoint resiliency in 2020. Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, observes that “Keeping machines up to date is an IT management job, but it’s a security outcome. Knowing what devices should be on my network is an IT management problem, but it has a security outcome. And knowing what’s going on and what processes are running and what’s consuming network bandwidth is an IT management problem, but it’s a security outcome. I don’t see these as distinct activities so much as seeing them as multiple facets of the same problem space, accelerating in 2020 as more enterprises choose greater resiliency to secure endpoints.”
  2. AI tools will continue to improve at drawing on data sets of wildly different types, allowing the “bigger picture” to be put together from, say, static configuration data, historic local logs, global threat landscapes, and contemporaneous event streams.  Nicko van Someren, Ph.D., and CTO at Absolute Software also predict that“Enterprise executives will be concentrating their budgets and time on detecting cyber threats using AI above predicting and responding. As enterprises mature in their use and adoption of AI as part of their cybersecurity efforts, prediction and response will correspondingly increase.”
  3. Threat actors will increase the use of AI to analyze defense mechanisms and simulate behavioral patterns to bypass security controls, leveraging analytics to and machine learning to hack into organizations. Dr. Torsten George, Cybersecurity Evangelist at Centrify, predicts that “threat actors, many of them state-sponsored, will increase their use and sophistication of AI algorithms to analyze organizations’’ defense mechanisms and tailor attacks to specific weak areas. He also sees the threat of bad actors being able to plug into the data streams of organizations and use the data to further orchestrate sophisticated attacks.”
  4. Given the severe shortage of experienced security operations resources and the sheer volume of data that most organizations are trying to work through, we are likely to see organizations seeking out AI/ML capabilities to automate their security operations processes. Craig Sanderson, Vice President of Security Products at Infoblox also predicts that “while AI and machine learning will increasingly be used to detect new threats it still leaves organizations with the task of understanding the scope, severity, and veracity of that threat to inform an effective response. As security operations becomes a big data problem it necessitates big data solutions.”
  5. There’s going to be a greater need for adversarial machine learning to combat supply chain corruption in 2020. Sean Tierney, Director of Threat Intelligence at Infoblox, predicts that “the need for adversarial machine learning to combat supply chain corruption is going to increase in 2020. Sean predicts that the big problem with remote coworking spaces is determining who has access to what data. As a result, AI will become more prevalent in traditional business processes and be used to identify if a supply chain has been corrupted.”
  6. Artificial intelligence will become more prevalent in account takeover—both the proliferation and prevention of it. Josh Johnston, Director of AI at Kount, predicts that “the average consumer will realize that passwords are not providing enough account protection and that every account they have is vulnerable. Captcha won’t be reliable either, because while it can tell if someone is a bot, it can’t confirm that the person attempting to log in is the account holder. AI can recognize a returning user. AI will be key in protecting the entire customer journey, from account creation to account takeover, to a payment transaction. And, AI will allow businesses to establish a relationship with their account holders that are protected by more than just a password.”
  7. Consumers will take greater control of their data sharing and privacy in 2020. Brian Foster, Senior Vice President Product Management at MobileIron, observes that over the past few years, we’ve witnessed some of the biggest privacy and data breaches. As a result of the backlash, tech giants such as Apple, Google, Facebook and Amazon beefed up their privacy controls to gain back trust from customers. Now, the tables have turned in favor of consumers and companies will have to put privacy first to stay in business. Moving forward, consumers will own their data, which means they will be able to selectively share it with third parties, but most importantly, they will get their data back after sharing, unlike in years past.
  8. As cybersecurity threats evolve, we’ll fight AI with AI. Brian Foster, Senior Vice President Product Management at MobileIron, notes that the most successful cyberattacks are executed by highly professional criminal networks that leverage AI and ML to exploit vulnerabilities such as user behavior or security gaps to gain access to valuable business systems and data. All of this makes it extremely hard for IT security organizations to keep up — much less stay ahead of these threats. While an attacker only needs to find one open door in an enterprise’s security, the enterprise must race to lock all of the doors. AI conducts this at a pace and thoroughness human ability can no longer compete with, and businesses will finally take notice in 2020.
  9. AI and machine learning will thwart compromised hardware finding its way into organizations’ supply chains. Rising demand for electronic components will expand the market for counterfeit components and cloned products, increasing the threat of compromised hardware finding its way into organizations’ supply chains. The vectors for hardware supply-chain attacks are expanding as market demand for more and cheaper chips, and components drive a booming business for hardware counterfeiters and cloners. This expansion is likely to create greater opportunities for compromise by both nation-state and cybercriminal threat actors. Source: 2020 Cybersecurity Threats Trends Outlook; Booz, Allen, Hamilton, 2019.
  10. Capgemini predicts 63% of organizations are planning to deploy AI in 2020 to improve cybersecurity, with the most popular application being network security. Capgemini found that nearly one in five organizations were using AI to improve cybersecurity before 2019. In addition to network security, data security, endpoint security, and identity and access management are the highest priority use cases for improving cybersecurity with AI in enterprises today. Source: Capgemini, Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security.
10 Predictions How AI Will Improve Cybersecurity In 2020

Source: Capgemini, Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security.

7 Ways AI Reduces Mobile Fraud Just In Time For The Holidays

7 Ways AI Reduces Mobile Fraud Just In Time For The Holidays

  • There has been a 680% increase in global fraud transactions from mobile apps from October 2015 to December 2018, according to RSA.
  •  70% of fraudulent transactions originated in the mobile channel in 2018.
  • RSA’s Anti-Fraud Command Center saw phishing attacks increase 178% after leading banks in Spain launched instant transfer services.
  • Rogue mobile apps are proliferating with, 20% of all reported cyberattacks originating from mobile apps in 2018 alone.

On average, there are 82 new rogue applications submitted per day to any given AppExchange or application platform, all designed to defraud consumers. Mobile and digital commerce are cybercriminals’ favorite attack surfaces because they are succeeding with a broad base of strategies for defrauding people and businesses.

Phishing, malware, smishing, or the use of SMS texts rather than email to launch phishing attempts are succeeding in gaining access to victims’ account credentials, credit card numbers, and personal information to launch identity theft breaches. The RSA is seeing an arms race between cybercriminals and mobile OS providers with criminals improving their malware to stay at parity or leapfrog new versions and security patches of mobile operating systems.

Improving Mobile Fraud Prevention With AI And Machine Learning

Creating a series of rogue applications and successfully uploading them into an AppExchange or application store gives cybercriminals immediate access to global markets. Hacking mobile apps and devices is one of the fastest-growing cybercriminal markets, one with 6.8B mobile users worldwide this year, projected to increase to 7.3B in 2023, according to The Radicati Group. The total number of mobile devices, including both phones and tablets, will be over 13B by the end of 2019, according to the research firm. And a small percentage of mobile fraud transactions get reported, with mobile fraud losses reported totaling just over $40M across 14,392 breaches according to the U.S. Federal Trade Commission. Mobile fraud is an epidemic that needs to be fought with state-of-the-art approaches based on AI and machine learning’s innate strengths.

Traditional approaches to thwarting digital fraud rely on rules engines that thrive on detecting and taking action based on established, known patterns, and are often hard-coded into a merchant’s system. Fraud analyst teams further customize rules engines to reflect the unique requirements of the merchants’ selling strategies across each channel. Fine-tuning rules engines makes them effective at recognizing and taking action on known threat patterns. The challenge for every merchant relying on a fraud rules engine is that they often don’t catch the latest patterns in cybercriminal activity. Where rules-based approaches to digital fraud don’t scale, AI, and machine learning do.

Exploring The 7 Ways AI Is Reducing Mobile Fraud

Where rules engines are best suited for spotting existing trends in fraud activity, machine learning excels at classifying observations (called supervised machine learning) and finding anomalies in data by finding entirely new patterns and associations (called unsupervised machine learning). Combining supervised and unsupervised machine learning algorithms are proving to be very effective at reducing mobile fraud. The following are the seven ways AI and machine learning are reducing mobile fraud today:

  1. AI and machine learning reduce false positives by interpreting the nuances of specific behaviors and accurately predicting if a transaction is fraudulent or not. Merchants are relying on AI and machine learning to reduce false positives, saving their customers from having to re-authenticate who they are and their payment method. A false positive at that first interaction with a customer is going to reduce the amount of money that they spend with a merchant, so it’s very important to interpret each transaction accurately.
  2. Identifying and thwarting merchant fraud based on anomalous activity from a compromised mobile device. Cybercriminals are relying on SIM swapping to gain control of mobile devices and commit fraud, as the recent hack of Twitter’s founder Jack Dorsey illustrates. Hackers were able to transfer his telephone number using SIM swapping and by talking Dorsey’s mobile service provider to bypass the account passcode. Fortunately, only his Twitter account was hacked. Any app or account accessible on his phone could have been breached, leading to fraudulent bank transfers or purchases. The attack could have been thwarted if Jack Dorsey’s mobile service provider was using AI-based risk scoring to detect and act on anomalous activity.
  3. AI and machine learning-based techniques scale across a wider breadth of merchants than any rules-based approach to mobile fraud prevention can. Machine learning-based models scale and learn across different industries in real-time, accumulating valuable data that improves payment fraud prediction accuracy. Kount’s Universal Data Network is noteworthy, as it includes billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. That rich data feeds Kount’s machine learning models to detect anomalies more accurately and reduce false positives and chargebacks.
  4. Combining supervised and unsupervised machine learning algorithms translates into a formidable speed advantage, with fraudulent transactions identified on average in 250 milliseconds. Merchants’ digital business models’ scale and speed are increasing, and with the holidays coming up, there’s a high probability many will set mobile commerce sales records. The merchants who will gain the most sales are focusing on how security and customer experience can complement each other. Being able to approve or reject a transaction within a second or less is the cornerstone of an excellent customer buying experience.
  5. Knowing when to use two-factor authentication via SMS or Voice PIN to reduce false negatives or not, preserving customer relationships in the process. Rules engines will often take a brute-force approach to authentication if any of the factors they’re tracking show a given transaction is potentially fraudulent. Requesting customers authenticate themselves after they’re logged into a merchant’s site when they attempt to buy an item is a sure way to lose a customer for life. By being able to spot anomalies quickly, fewer customers are forced to re-authenticate themselves, and customer relationships are preserved. And when transactions are indeed fraudulent, losses have been averted in less than a second.
  6. Provide a real-time transaction risk score that combines the strengths of supervised and unsupervised machine learning into a single fraud prevention payment score. Merchants need a real-time transaction risk score that applies to every channel they sell, though. Fraud rules engines had to be tailored to each specific selling channel with specific rules for each type of transaction. That’s no longer the case due to machine learnings’ ability to scale across all channels and provide a transaction risk score in milliseconds. Leaders in this area include Kount’s Omniscore, the actionable transaction safety rating that is a result of their AI, which combines patented, proprietary supervised and unsupervised machine learning algorithms and technologies.
  7. Combining insights from supervised and unsupervised machine learning with contextual intelligence of transactions frees up fraud analysts to do more investigations and fewer transaction reviews. AI and machine learning-based fraud prevention systems’ first contribution is often reducing the time fraud analysts take for manual reviews. Digitally-based businesses I’ve talked with say having supervised machine learning categorize and then predict fraudulent attempts is invaluable from a time-saving standpoint alone. Merchants are finding AI, and machine learning-based approaches enable to score to approve more orders automatically, reject more orders automatically, and focus on those gray area orders, freeing up fraud analysts to do more strategic, rewarding work. They’re able to find more sophisticated, nuanced abuse attacks like refer a friend abuse or a promotion abuse or seller collusion in a marketplace. Letting the model do the work of true payment fraud prevention frees up those fraud analysts to do other worth that add value.

Conclusion

With the holiday season rapidly approaching, it’s time for merchants to look at how they can protect mobile transactions at scale across all selling channels. AI and machine learning are proving themselves as viable replacements to traditional rules engines that rely on predictable, known fraud patterns. With 70% of fraudulent transactions originating in the mobile channel in 2018 and the influx of orders coming in the next three months, now would be a good time for merchants to increase their ability to thwart mobile fraud while reducing false positives that alienate customers.

Sources:

RSA 2019 Current State of Cybercrime Report (11 pp., PDF, opt-in)

The Radicati Group, Mobile Statistics Report, 2019 – 2023 (3 pp., PDF, no opt-in)

U.S. Federal Trade Commission, Consumer Sentinel Network, Data Book 2018 (90 pp., PDF, no opt-in)

 

 

7 Signs It’s Time To Get Focused On Zero Trust

7 Signs It’s Time To Get Focused On Zero Trust

When an experienced hacker can gain access to a company’s accounting and financial systems in 7 minutes or less after obtaining privileged access credentials, according to Ponemon, it’s time to get focused on Zero Trust Security. 2019 is on its way to being a record year for ransomware attacks, which grew 118% in Q1 of this year alone, according to McAfee Labs Threat Report. Data breaches on healthcare providers reached an all-time high in July of this year driven by the demand for healthcare records that range in price from $250 to over $1,000 becoming best-sellers on the Dark Web. Cybercriminals are using AI, bots, machine learning, and social engineering techniques as part of sophisticated, well-orchestrated strategies to gain access to banking, financial services, healthcare systems, and many other industries’ systems today.

Enterprises Need Greater Urgency Around Zero Trust

The escalating severity of cyberattacks and their success rates are proving that traditional approaches to cybersecurity based on “trust but verify” aren’t working anymore. What’s needed is more of a Zero Trust-based approach to managing every aspect of cybersecurity. By definition, Zero Trust is predicated on a “never trust, always verify” approach to access, from inside or outside the network. Enterprises need to begin with a Zero Trust Privilege-based strategy that verifies who is requesting access, the context of the request, and the risk of the access environment.

How urgent is it for enterprises to adopt Zero Trust? A recent survey of 2,000 full-time UK workers, completed by Censuswide in collaboration with Centrify, provides seven signs it’s time for enterprises to get a greater sense of urgency regarding their Zero Trust frameworks and initiatives. The seven signs are as follows:

  1. 77% of organizations’ workers admit that they have never received any form of cybersecurity skills training from their employer. In this day and age, it’s mind-blowing that three of every four organizations aren’t providing at least basic cybersecurity training, whether they intend to adopt Zero Trust or not. It’s like freely handing out driver’s licenses to anyone who wants one so they can drive the freeways of Los Angeles or San Francisco. The greater the training, the safer the driver. Likewise, the greater the cybersecurity training, the safer the worker, company and customers they serve.
  2. 69% of employees doubt the cybersecurity processes in place in their organizations today. When the majority of employees don’t trust the security processes in place in an organization, they invent their own, often bringing their favorite security solutions into an enterprise. Shadow IT proliferates, productivity often slows down, and enterprise is more at risk of a breach than ever before. When there’s no governance or structure to managing data, cybercriminals flourish.
  3. 63% of British workers interviewed do not realize that unauthorized access to an email account without the owner’s permission is a criminal offense. It’s astounding that nearly two-thirds of the workers in an organization aren’t aware that unauthorized access to another person’s email account without their permission is a crime. The UK passed into law 30 years ago the Computer Misuse Act. The law was created to protect individuals’ and organizations’ electronic data. The Act makes it a crime to access or modify data stored on a computer without authorization to do so. The penalties are steep for anyone found guilty of gaining access to a computer without permission, starting with up to two years in prison and a £5,000 fine. It’s alarming how high the lack of awareness is of this law, and an urgent call to action to prioritize organization-wide cybersecurity training.
  4. 27% of workers use the same password for multiple accounts. The Consensus survey finds that workers are using identical passwords for their work systems, social media accounts, and both personal and professional e-mail accounts. Cybersecurity training can help reduce this practice, but Zero Trust is badly needed to protect privileged access credentials that may have identical passwords to someone’s Facebook account, for example.
  5. 14% of employees admitted to keeping their passwords recorded in an unsecured handwritten notebook or on their desk in the office.  Organizations need to make it as difficult as possible for bad actors and cybercriminals to gain access to passwords instead of sharing them in handwritten notebooks and on Post-It notes. Any organization with this problem needs to immediately adopt Multi-Factor Authentication (MFA) as an additional security measure to ensure compromised passwords don’t lead to unauthorized access. For privileged accounts, use a password vault, which can make handwritten password notes (and shared passwords altogether) obsolete.
  6. 14% do not use multi-factor authentication for apps or services unless forced to do so. Centrify also found that 58% of organizations do not use Multi-Factor Authentication (MFA) for privileged administrative access to servers, leaving their IT systems and infrastructure unsecured. Not securing privileged access credentials with MFA or, at the very least, vaulting them is like handing the keys to the kingdom to cybercriminals going after privileged account access. Securing privileged credentials needs to begin with a Zero Trust-based approach that verifies who is requesting access, the context of the request, and the risk of the access environment.
  7. 1 out of every 25 employees hacks into a colleague’s email account without permission. In the UK, this would be considered a violation of the Computer Misuse Act, which has some unfortunate outcomes for those found guilty of violating it. The Censuswide survey also found that one in 20 workers have logged into friend’s Facebook accounts without permission. If you work in an organization of over 1,000 people, for example, 40 people in your company have most likely hacked into a colleague’s email account, opening up your entire company to legal liability.

Conclusion

Leaving cybersecurity to chance and hoping employees will do the right thing isn’t a strategy; it’s an open invitation to get hacked. The Censuswide survey and many others like it reflect a fundamental truth that cybersecurity needs to become part of the muscle memory of any organization to be effective. As traditional IT network perimeters dissolve, enterprises need to replace “trust but verify” with a Zero Trust-based framework. Zero Trust Privilege mandates a “never trust, always verify, enforce least privilege” approach to privileged access, from inside or outside the network. Leaders in this area include Centrify, who combines password vaulting with brokering of identities, multi-factor authentication enforcement, and “just enough” privilege, all while securing remote access and monitoring of all privileged sessions.

10 Charts That Will Change Your Perspective Of AI In Security

10 Charts That Will Change Your Perspective Of AI In Security

Rapid advances in AI and machine learning are defining cybersecurity’s future daily. Identities are the new security perimeter and Zero Trust Security frameworks are capitalizing on AI’s insights to thwart breaches in milliseconds. Advances in AI and machine learning are also driving the transformation of endpoint security toward greater accuracy and contextually intelligence.

69% of enterprise executives believe artificial intelligence (AI) will be necessary to respond to cyberattacks with the majority of telecom companies (80%) saying they are counting on AI to help identify threats and thwart attacks according to Capgemini. Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%. Cloud Security, Data Security, and Infrastructure Protection are the fastest-growing areas of security spending through 2023. The following ten charts illustrate the market and technological factors driving the rapid growth of AI in security today:

  • AI shows the greatest potential for fraud detection, malware detection, assigning risk scores to login attempts on networks, and intrusion detection. Supervised and unsupervised machine learning algorithms are proving to be effective in identifying potentially fraudulent online transaction activity. By definition, supervised machine learning algorithms rely on historical data to find patterns not discernible with traditional rule-based approaches to fraud detection. Finding anomalies, interrelationships, and valid links between emerging factors and variables is unsupervised machine learning’s core strength. Combining each is proving to be very effective in identifying anomalous behavior and reducing or restricting access. Kount’s  Omniscore relies on these technologies to provide an AI-driven transaction safety rating. Source: Capgemini Research Institute, Reinventing Cybersecurity with Artificial Intelligence – The new frontier in digital security (28 pp., PDF, no opt-in).
  • 80% of telecommunications executives stated that they believe their organization would not be able to respond to cyberattacks without AI. Across all seven industries studied in a recent Capgemini survey, 69% of all senior executives say they would not be able to respond to a cyberattack without AI. 75% of banking executives realize they’ll need AI to thwart a cyberattack. Interestingly, 59% of Utilities executives, the lowest response to this question on the survey, see AI as essential for battling a cyberattack. Utilities are one of the more vulnerable industries to attacks given their legacy infrastructure. Source: Statistica, Share of organizations that rely on artificial intelligence (AI) for cybersecurity in selected countries as of 2019, by industry
  • 51% of enterprises primarily rely on AI for threat detection, leading prediction, and response. Consistent with the majority of cybersecurity surveys of enterprises’ AI adoption for cybersecurity in 2019, AI is relied the majority of the time for detecting threats. A small percentage of enterprises have progressed past detection to prediction and response, as the graphic below shows. Many of the more interesting AI projects today are in prediction and response, given how the challenges in these areas expand the boundaries of technologies fast. Source: Capgemini Research Institute, Reinventing Cybersecurity with Artificial Intelligence – The new frontier in digital security (28 pp., PDF, no opt-in).
  • Enterprises are relying on AI as the foundation of their security automation frameworks. AI-driven security automation frameworks are designed to flex and support new digital business models across an organization. Existing security automation frameworks can crunch and correlate threat patterns on massive volumes of disparate data, which introduces opportunities for advanced cybersecurity without disrupting business. Using alerts and prescriptive analytics for dynamic policies to address identified risks, enterprises can speed deployment of threat-blocking measures, increasing the agility of security operations. Source: Cognizant, Combating Cybersecurity Challenges with Advanced Analytics (PDF, 24 pp., no opt-in).
  • Cybersecurity leads all other investment categories this year of TD Ameritrade’s Registered Investment Advisors (RIA) Survey. The survey found RIAs are most interested in investment opportunities for their clients in AI-based cybersecurity new ventures. Source: TD Ameritrade Institutional 2019 RIA Sentiment Survey (PDF, 35 pp., no opt-in)
  • 62% of enterprises have adopted and implemented AI to its full potential for cybersecurity, or are still exploring additional uses. AI is gaining adoption in U.S.-based enterprises and is also being recommended by government policy influencers. Just 21% of enterprises have no plans for using AI-based cybersecurity today.  Source: Oracle, Security In the Age Of AI (18 pp., PDF. no opt-in
  • 71% of today’s organizations reporting they spend more on AI and machine learning for cybersecurity than they did two years ago. 26% and 28% of U.S. and Japanese IT professionals believe their organizations could be doing more. Additionally, 84% of respondents believe cyber-criminals are also using AI and ML to launch their attacks. When considered together, these figures indicate a strong belief that AI/ML based cybersecurity is no longer simply nice to have; it’s crucial to stop modern cyberattacks.   Source: Webroot, Knowledge Gaps: AI and Machine Learning in CyberSecurity Perspectives from the U.S. and Japanese IT Professionals (PDF, 9 pp., no opt-in)
  • 73% of enterprises have adopted security products with some form of AI integrated into them. Among enterprises that receive more than 1,000 alerts per day, the percentage that has AI-enabled products in their security infrastructure jumps to 84%. The findings suggest that some decision makers view AI as useful capability in dealing with the flood of alerts that they receive. Source: Osterman Research, The State of AI in Cybersecurity: The Benefits, Limitations and Evolving Questions (PDF, 10 pp., opt-in).
  • AI’s greatest benefit is the increase in the speed of analyzing threats (69%) followed by an acceleration in the containment of infected endpoints/devices and hosts (64%). Because AI reduces the time to respond to cyber exploits organizations can potentially save an average of more than $2.5 million in operating costs. Source: The Value of Artificial Intelligence in Cybersecurity – Sponsored by IBM Security Independently conducted by Ponemon Institute LLC, July 2018.

5 Proven Ways Manufacturers Can Get Started With Analytics

5 Proven Ways Manufacturers Can Get Started With Analytics

Going into 2020, manufacturers are at an inflection point in their adoption of analytics and business intelligence (BI). Analytics applications and tools make it possible for them to gain greater insights from the massive amount of data they produce every day. And with manufacturing leading all industries on the planet when it comes to the amount of data generated from operations daily, the potential to improve shop floor productivity has never been more within reach for those adopting analytics and BI applications.

Analytics and BI Are High Priorities In Manufacturing Today

Increasing the yield rates and quality levels for each shop floor, machine and work center is a high priority for manufacturers today. Add to that the pressure to stay flexible and take on configure-to-order and engineer-to-order special products fulfilled through short-notice production runs and the need for more insight into how each phase of production can be improved. Gartner’s latest survey of heavy manufacturing CIOs in the 2019 CIO Agenda: Heavy Manufacturing, Industry Insights, by Dr. Marc Halpern. October 15, 2018 (Gartner subscription required) reflects the reality all manufacturers are dealing with today. I believe they’re in a tough situation with customers wanting short-notice production time while supply chains often needing to be redesigned to reduce or eliminate tariffs. They’re turning to analytics to gain the insights they need to take on these challenges and more. The graphic below is from Gartner’s latest survey of heavy manufacturing CIOs, it indicates the technology areas where heavy manufacturing CIOs’ organizations will be spending the largest amount of new or additional funding in 2019 as well as the technology areas where their organizations will be reducing funding by the highest amount in 2019 compared with 2018:

Knowing Which Problems To Solve With Analytics

Manufacturers getting the most value from analytics start with a solid business case first, based on a known problem they’ve been trying to solve either in their supply chains, production or fulfillment operations. The manufacturers I’ve worked with focus on how to get more orders produced in less time while gaining greater visibility across production operations. They’re all under pressure to stay in compliance with customers and regulatory reporting; in many cases needing to ship product quality data with each order and host over 60 to 70 audits a year from customers in their plants. Analytics is becoming popular because it automates the drudgery of reporting that would otherwise take IT team’s days or weeks to do manually.

As one CIO put it as we walked his shop floor, “we’re using analytics to do the heavy data crunching when we’re hosting customer audits so we can put our quality engineers to work raising the bar of product excellence instead of having them run reports for a week.” As we walked the shop floor he explained how dashboards are tailored to each role in manufacturing, and the flat-screen monitors provide real-time data on how five key areas of performance are doing. Like many other CIOs facing the challenge of improving production efficiency and quality, he’s relying on the five core metrics below in the initial roll-out of analytics across manufacturing operations, finance, accounting, supply chain management, procurement, and service:

  • Manufacturing Cycle Time – One of the most popular metrics in manufacturing, Cycle Time quantifies the amount of elapsed time from when an order is placed until the product is manufactured and entered into finished goods inventory. Cycle times vary by segment of the manufacturing industry, size of manufacturing operation, global location and relative stability of supply chains supporting operations. Real-time integration, applying Six Sigma to know process bottlenecks, and re-engineering systems to be more customer-focused improve this metrics’ performance. Cycle Time is a predictor of the future of manufacturing as this metric captures improvement made across systems and processes immediately.
  • Supplier Inbound Quality Levels – Measuring the dimensions of how effective a given supplier is at consistently meeting a high level of product quality and on-time delivery is valuable in orchestrating a stable supply chain. Inbound quality levels often vary from one shipment to the next, so it’s helpful to have Statistical Process Control (SPC) charts that quantify and show the trends of quality levels over time. Nearly all manufacturers are relying on Six Sigma programs to troubleshoot specific trouble spots and problem areas of suppliers who may have wide variations in product quality in a given period. This metric is often used for ranking which suppliers are the most valuable to a factory and production network as well.
  • Production Yield Rates By Product, Process, and Plant Location – Yield rates reflect how efficient a machine or entire process is in transforming raw materials into finished products. Manufacturers rely on automated and manually-based approaches to capture this metric, with the latest generation of industrial machinery capable of producing its yield rate levels over time. Process-related manufacturers rely on this metric to manage every production run they do. Microprocessors, semiconductors, and integrated circuit manufacturers are continually monitoring yield rates to determine how they are progressing against plans and goals. Greater real-time integration, improved quality management systems, and greater supply chain quality and compliance all have a positive impact on yield rates. It’s one of the key measures of production yield as it reflects how well-orchestrated entire production processes are.
  • Perfect Order Performance – Perfect order performance measures how effective a manufacturer is at delivering complete, accurate, damage-free orders to customers on time. The equation that defines the perfect order Index (POI) or perfect order performance is the (Percent of orders delivered on time) * (Percent of orders complete) * (Percent of orders damage free) * (Percent of orders with accurate documentation) * 100. The majority of manufacturers are attaining a perfect order performance level of 90% or higher, according to The American Productivity and Quality Center (APQC). The more complex the product lines, configuration options, including build-to-order, configure-to-order, and engineer-to-order, the more challenging it is to attain a high, perfect order level. Greater analytics and insights gained from real-time integration and monitoring help complex manufacturers attained higher perfect order levels over time.
  • Return Material Authorization (RMA) Rate as % Of Manufacturing – The purpose of this metric is to define the percentage of products shipped to customers that are returned due to defective parts or not otherwise meeting their requirements. RMAs are a good leading indicator of potential quality problems. RMAs are also a good measure of how well integrated PLM, ERP and CRM systems, resulting in fewer product errors.

Conclusion

The manufacturers succeeding with analytics start with a compelling business case, one that has an immediate impact on the operations of their organizations. CIOs are prioritizing analytics and BI to gain greater insights and visibility across every phase of manufacturing. They’re also adopting analytics and BI to reduce the reporting drudgery their engineering, IT, and manufacturing teams are faced with as part of regular customer audits. There are also a core set of metrics manufacturers rely on to manage their business, and the five mentioned here are where many begin.

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