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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)

 

 

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.

What’s New In Gartner’s Hype Cycle For AI, 2019

What's New In Gartner's Hype Cycle For AI, 2019

  • Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner’s 2019 CIO Agenda survey.
  • Conversational AI remains at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant, and others.
  • Enterprises are making progress with AI as it grows more widespread, and they’re also making more mistakes that contribute to their accelerating learning curve.

These and many other new insights are from Gartner Hype Cycle For AI, 2019 published earlier this year and summarized in the recent Gartner blog post, Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019.  Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here. Gartner’s latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. The Gartner Hype Cycle for AI, 2019, is shown below:

Details Of What’s New In Gartner’s Hype Cycle For AI, 2019

  • Speech Recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits of all technologies on the Hype Cycle. Gartner advises its clients to consider including speech recognition on their short-term AI technology roadmaps. Gartner observes, unlike other technologies within the natural-language processing area, speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows. Leading vendors in this technology area Amazon, Baidu, Cedat 85, Google, IBM, Intelligent Voice, Microsoft, NICE, Nuance, and Speechmatics.
  • Eight new AI-based technologies are included in this year’s Hype Cycle, reflecting Gartner enterprise clients’ plans to scale AI across DevOps and IT while supporting new business models. The latest technologies to be included in the Hype Cycle for AI reflect how enterprises are trying to demystify AI to improve adoption while at the same time, fuel new business models. The new technologies include the following:
  1. AI Cloud Services – AI cloud services are hosted services that allow development teams to incorporate the advantages inherent in AI and machine learning.
  2. AutoML – Automated machine learning (AutoML) is the capability of automating the process of building, deploying, and managing machine learning models.
  3. Augmented Intelligence – Augmented intelligence is a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.
  4. Explainable AI – AI researchers define “explainable AI” as an ensemble of methods that make black-box AI algorithms’ outputs sufficiently understandable.
  5. Edge AI – Edge AI refers to the use of AI techniques embedded in IoT endpoints, gateways, and edge devices, in applications ranging from autonomous vehicles to streaming analytics.
  6. Reinforcement Learning – Reinforcement learning has the primary potential for gaming and automation industries and has the potential to lead to significant breakthroughs in robotics, vehicle routing, logistics, and other industrial control scenarios.
  7. Quantum Computing – Quantum computing has the potential to make significant contributions to the areas of systems optimization, machine learning, cryptography, drug discovery, and organic chemistry. Although outside the planning horizon of most enterprises, quantum computing could have strategic impacts in key businesses or operations.
  8. AI Marketplaces – Gartner defines an AI Marketplace as an easily accessible place supported by a technical infrastructure that facilitates the publication, consumption, and billing of reusable algorithms. Some marketplaces are used within an organization to support the internal sharing of prebuilt algorithms among data scientists.
  • Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.
  • Smart Robots and AutoML are at the peak of the Hype Cycle in 2019. In contrast to the rapid growth of industrial robotics systems that adopted by manufacturers due to the lack of workers, Smart Robots are defined by Gartner as having electromechanical form factors that work autonomously in the physical world, learning in short-term intervals from human-supervised training and demonstrations or by their supervised experiences including taking direction form human voices in a shop floor environment. Whiz robot from SoftBank Robotics is an example of a SmartRobot that will be sold under robot-as-a service (RaaS) model and originally be available only in Japan. AutoML is one of the most hyped technology in AI this year. Gartner defines automated machine learning (AutoML) as the capability of automating the process of building, deploying, or managing machine learning models. Leading vendors providing AutoML platforms and applications include Amazon SageMaker, Big Squid, dotData, DataRobot, Google Cloud Platform, H2O.ai, KNIME, RapidMiner, and Sky Tree.
  • Nine technologies were removed or reassigned from this years’ Hype Cycle of AI compared to 2018. Gartner has removed nine technologies, often reassigning them into broader categories. Augmented reality and Virtual Reality are now part of augmented intelligence, a more general category, and remains on many other Hype Cycles. Commercial UAVs (drones) is now part of edge AI, a more general category. Ensemble learning had already reached the Plateau in 2018 and has now graduated from the Hype Cycle. Human-in-the-loop crowdsourcing has been replaced by data labeling and annotation services, a broader category. Natural language generation is now included as part of NLP. Knowledge management tools have been replaced by insight engines, which are more relevant to AI. Predictive analytics and prescriptive analytics are now part of decision intelligence, a more general category.

Sources:

Hype Cycle for Artificial Intelligence, 2019, Published 25 July 2019, (Client access reqd.)

Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019 published September 12, 2019

State Of AI And Machine Learning In 2019

  • Marketing and Sales prioritize AI and machine learning higher than any other department in enterprises today.
  • In-memory analytics and in-database analytics are the most important to Finance, Marketing, and Sales when it comes to scaling their AI and machine learning modeling and development efforts.
  • R&D’s adoption of AI and machine learning is the fastest of all enterprise departments in 2019.

These and many other fascinating insights are from Dresner Advisory Services’6th annual 2019 Data Science and Machine Learning Market Study (client access reqd) published last month. The study found that advanced initiatives related to data science and machine learning, including data mining, advanced algorithms, and predictive analytics are ranked the 8th priority among the 37 technologies and initiatives surveyed in the study. Please see page 12 of the survey for an overview of the methodology.

“The Data Science and Machine Learning Market Study is a progression of our analysis of this market which began in 2014 as an examination of advanced and predictive analytics,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “Since that time, we have expanded our coverage to reflect changes in sentiment and adoption, and have added new criteria, including a section covering neural networks.”

Key insights from the study include the following:

  • Data mining, advanced algorithms, and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning in 2019. Reporting, dashboards, data integration, and advanced visualization are the leading technologies and initiatives strategic to Business Intelligence (BI) today. Cognitive BI (artificial-intelligence-based BI) ranks comparatively lower at 27th among priorities. The following graphic prioritizes the 27 technologies and initiatives strategic to business intelligence:

  • 40% of Marketing and Sales teams say data science encompassing AI and machine learning is critical to their success as a department. Marketing and Sales lead all departments in how significant they see AI and machine learning to pursue and accomplish their growth goals. Business Intelligence Competency Centers (BICC), R&D, and executive management audiences are the next most interested, and all top four roles cited carry comparable high combined “critical” and “very important” scores above 60%. The following graphic compares the importance levels by department for data science, including AI and machine learning:

  • R&D, Marketing, and Sales’ high level of shared interest across multiple feature areas reflect combined efforts to define new revenue growth models using AI and machine learning. Marketing, Sales, R&D, and the Business Intelligence Competency Centers (BICC) respondents report the most significant interest in having a range of regression models to work with in AI and machine learning applications. Marketing and Sales are also most interested in the next three top features, including hierarchical clustering, textbook statistical functions, and having a recommendation engine included in the applications and platforms they purchase. Dresner’s research team believes that the high shared interest in multiple features areas by R&D, Marketing and Sales is leading indicator enterprises are preparing to pilot AI and machine learning-based strategies to improve customer experiences and drive revenue. The following graphic compares interest and probable adoption by functional area of the enterprises interviewed:

  • 70% of R&D departments and teams are most likely to adopt data science, AI, and machine learning, leading all functions in an enterprise. Dresner’s research team sees the high level of interest by R&D teams as a leading indicator of broader enterprise adoption in the future. The study found 33% of all enterprises interviewed have adopted AI and machine learning, with the majority of enterprises having up to 25 models. Marketing & Sales lead all departments in their current evaluation of data science and machine learning software.

  • Financial Services & Insurance, Healthcare, and Retail/Wholesale say data science, AI, and machine learning are critical to their succeeding in their respective industries. 27% of Financial Services & Insurance, 25% of Healthcare and 24% of Retail/Wholesale enterprises say data science, AI, and machine learning are critical to their success. Less than 10% of Educational institutions consider AI and machine learning vital to their success. The following graphic compares the importance of data science, AI, and machine learning by industry:

  • The Telecommunications industry leads all others in interest and adoption of recommendation engines and model management governance. The Telecommunications, Financial Services, and Technology industries have the highest level of interest in adopting a range of regression models and hierarchical clustering across all industry respondent groups interviewed. Healthcare respondents have much lower interest in these latter features but high interest in Bayesian methods and text analytics functions. Retail/Wholesale respondents are often least interested in analytical features. The following graphic compares industries by their level of interest and potential adoption of analytical features in data science, AI, and machine learning applications and platforms:

  • Support for a broad range of regression models, hierarchical clustering, and commonly used textbook statistical functions are the top features enterprises need in data science and machine learning platforms. Dresner’s research team found these three features are considered the most important or “must-have” when enterprises are evaluating data science, AI and machine learning applications and platforms. All enterprises surveyed also expect any data science application or platform they are evaluating to have a recommendation engine included and model management and governance. The following graphic prioritizes the most and least essential features enterprises expect to see in data science, AI, and machine learning software and platforms:

  • The top three usability features enterprises are prioritizing today include support for easy iteration of models, access to advanced analytics, and an initiative, simple process for continuous modification of models. Support and guidance in preparing analytical data models and fast cycle time for analysis with data preparation are among the highest- priority usability features enterprises expect to see in AI and machine learning applications and platforms. It’s interesting to see the usability attribute of a specialist not required to create analytical models, test and run them at the lower end of the usability rankings. Many AI and machine learning software vendors rely on not needing a specialist to use their applications as a differentiator when the majority of enterprises value  support for easy iteration of models at a higher level as the graphic below shows:

  • 2019 is a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals. Enterprises most expect AI and machine learning applications and platforms to support a range of regression models, followed by hierarchical clustering and textbook statistical functions for descriptive statistics. Recommendation engines are growing in popularity as interest grew to at least a tie as the second most important feature to respondents in 2019. Geospatial analysis and Bayesian methods were flat or slightly less important compared to 2018. The following graphic compares six years of interest in data science, AI, and machine learning techniques:

How AI Is Protecting Against Payments Fraud

  • 80% of fraud specialists using AI-based platforms believe the technology helps reduce payments fraud.
  • 63.6% of financial institutions that use AI believe it is capable of preventing fraud before it happens, making it the most commonly cited tool for this purpose.
  • Fraud specialists unanimously agree that AI-based fraud prevention is very effective at reducing chargebacks.
  • The majority of fraud specialists (80%) have seen AI-based platforms reduce false positives, payments fraud, and prevent fraud attempts.

AI is proving to be very effective in battling fraud based on results achieved by financial institutions as reported by senior executives in a recent survey, AI Innovation Playbook published by PYMNTS in collaboration with Brighterion. The study is based on interviews with 200 financial executives from commercial banks, community banks, and credit unions across the United States. For additional details on the methodology, please see page 25 of the study. One of the more noteworthy findings is that financial institutions with over $100B in assets are the most likely to have adopted AI, as the study has found 72.7% of firms in this asset category are currently using AI for payment fraud detection.

Taken together, the findings from the survey reflect how AI thwarts payments fraud and deserves to be a high priority in any digital business today. Companies, including Kount and others, are making strides in providing AI-based platforms, further reducing the risk of the most advanced, complex forms of payments fraud.

Why AI Is Perfect For Fighting Payments Fraud

Of the advanced technologies available for reducing false positives, reducing and preventing fraud attempts, and reducing manual reviews of potential payment fraud events, AI is ideally suited to provide the scale and speed needed to take on these challenges. More specifically, AI’s ability to interpret trend-based insights from supervised machine learning, coupled with entirely new knowledge gained from unsupervised machine learning algorithms are reducing the incidence of payments fraud. By combining both machine learning approaches, AI can discern if a given transaction or series of financial activities are fraudulent or not, alerting fraud analysts immediately if they are and taking action through predefined workflows. The following are the main reasons why AI is perfect for fighting payments fraud:

  • Payments fraud-based attacks are growing in complexity and often have a completely different digital footprint or pattern, sequence, and structure, which make them undetectable using rules-based logic and predictive models alone. For years e-commerce sites, financial institutions, retailers, and every other type of online business relied on rules-based payment fraud prevention systems. In the earlier years of e-commerce, rules and simple predictive models could identify most types of fraud. Not so today, as payment fraud schemes have become more nuanced and sophisticated, which is why AI is needed to confront these challenges.
  • AI brings scale and speed to the fight against payments fraud, providing digital businesses with an immediate advantage in battling the many risks and forms of fraud. What’s fascinating about the AI companies offering payments fraud solutions is how they’re trying to out-innovate each other when it comes to real-time analysis of transaction data. Real-time transactions require real-time security. Fraud solutions providers are doubling down on this area of R&D today, delivering impressive results. The fastest I’ve seen is a 250-millisecond response rate for calculating risk scores using AI on the Kount platform, basing queries on a decades-worth of data in their universal data network. By combining supervised and unsupervised machine learning algorithms, Kount is delivering fraud scores that are twice as predictive as previous methods and faster than competitors.
  • AI’s many predictive analytics and machine learning techniques are ideal for finding anomalies in large-scale data sets in seconds. The more data a machine learning model has to train on, the more accurate its predictive value. The greater the breadth and depth of data, a given machine learning algorithm learns from means more than how advanced or complex a given algorithm is. That’s especially true when it comes to payments fraud detection where machine learning algorithms learn what legitimate versus fraudulent transactions look like from a contextual intelligence perspective. By analyzing historical account data from a universal data network, supervised machine learning algorithms can gain a greater level of accuracy and predictability. Kount’s universal data network is among the largest, including billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. The data network includes different transaction complexities, verticals, and geographies, so machine learning models can be properly trained to predict risk accurately. That analytical richness includes data on physical real-world and digital identities creating an integrated picture of customer behavior.

Bottom Line:  Payments fraud is insidious, difficult to stop, and can inflict financial harm on any business in minutes. Battling payment fraud needs to start with a pre-emptive strategy to thwart fraud attempts by training machine learning models to quickly spot and act on threats then building out the strategy across every selling and service channel a digital business relies on.

Why AI Is The Future Of Cybersecurity

These and many other insights are from Capgemini’s Reinventing Cybersecurity with Artificial Intelligence Report published this week. You can download the report here (28 pp., PDF, free, no opt-in). Capgemini Research Institute surveyed 850 senior executives from seven industries, including consumer products, retail, banking, insurance, automotive, utilities, and telecom. 20% of the executive respondents are CIOs, and 10% are CISOs. Enterprises headquartered in France, Germany, the UK, the US, Australia, the Netherlands, India, Italy, Spain, and Sweden are included in the report. Please see page 21 of the report for a description of the methodology.

Capgemini found that as digital businesses grow, their risk of cyberattacks exponentially increases. 21% said their organization experienced a cybersecurity breach leading to unauthorized access in 2018. Enterprises are paying a heavy price for cybersecurity breaches: 20% report losses of more than $50 million. Centrify’s most recent survey, Privileged Access Management in the Modern Threatscape, found that 74% of all breaches involved access to a privileged account. Privileged access credentials are hackers’ most popular technique for initiating a breach to exfiltrate valuable data from enterprise systems and sell it on the Dark Web.

Key insights include the following:

  • 69% of enterprises believe AI will be necessary to respond to cyberattacks. The majority of telecom companies (80%) say they are counting on AI to help identify threats and thwart attacks. Capgemini found the telecom industry has the highest reported incidence of losses exceeding $50M, making AI a priority for thwarting costly breaches in that industry. It’s understandable by Consumer Products (78%), and Banking (75%) are 2nd and 3rd given each of these industry’s growing reliance on digitally-based business models. U.S.-based enterprises are placing the highest priority on AI-based cybersecurity applications and platforms, 15% higher than the global average when measured on a country basis.

  • 73% of enterprises are testing use cases for AI for cybersecurity across their organizations today with network security leading all categories. Endpoint security the 3rd-highest priority for investing in AI-based cybersecurity solutions given the proliferation of endpoint devices, which are expected to increase to over 25B by 2021. Internet of Things (IoT) and Industrial Internet of Things (IIoT) sensors and systems they enable are exponentially increasing the number of endpoints and threat surfaces an enterprise needs to protect. The old “trust but verify” approach to enterprise security can’t keep up with the pace and scale of threatscape growth today. Identities are the new security perimeter, and they require a Zero Trust Security framework to be secure. Be sure to follow Chase Cunningham of Forrester, Principal Analyst, and the leading authority on Zero Trust Security to keep current on this rapidly changing area. You can find his blog here.

  • 51% of executives are making extensive AI for cyber threat detection, outpacing prediction, and response by a wide margin. Enterprise executives are 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. “AI tools are also getting better 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,” said Nicko van Someren, Chief Technology Officer at Absolute Software.

  • 64% say that AI lowers the cost to detect and respond to breaches and reduces the overall time taken to detect threats and breaches up to 12%. The reduction in cost for a majority of enterprises ranges from 1% – 15% (with an average of 12%). With AI, the overall time taken to detect threats and breaches is reduced by up to 12%. Dwell time – the amount of time threat actors remain undetected – drops by 11% with the use of AI. This time reduction is achieved by continuously scanning for known or unknown anomalies that show threat patterns. PetSmart, a US-based specialty retailer, was able to save up to $12M by using AI in fraud detection from Kount. By partnering with Kount, PetSmart was able to implement an AI/Machine Learning technology that aggregates millions of transactions and their outcomes. The technology determines the legitimacy of each transaction by comparing it against all other transactions received. As fraudulent orders were identified, they were canceled, saving the company money and avoiding damage to the brand. The top 9 ways Artificial Intelligence prevents fraud provides insights into how Kount’s approach to unsupervised and supervised machine learning stops fraud.

  • Fraud detection, malware detection, intrusion detection, scoring risk in a network, and user/machine behavioral analysis are the five highest AI use cases for improving cybersecurity. Capgemini analyzed 20 use cases across information technology (IT), operational technology (OT) and the Internet of Things (IoT) and ranked them according to their implementation complexity and resultant benefits (in terms of time reduction). Based on their analysis, we recommend a shortlist of five high-potential use cases that have low complexity and high benefits. 54% of enterprises have already implemented five high impact cases. The following graphic compares the recommended use cases by the level of benefit and relative complexity.

  • 56% of senior execs say their cybersecurity analysts are overwhelmed and close to a quarter (23%) are not able to successfully investigate all identified incidents. Capgemini found that hacking organizations are successfully using algorithms to send ‘spear phishing’ tweets (personalized tweets sent to targeted users to trick them into sharing sensitive information). AI can send the tweets six times faster than a human and with twice the success. “It’s no surprise that Capgemini’s data shows that security analysts are overwhelmed. The cybersecurity skills shortage has been growing for some time, and so have the number and complexity of attacks; using machine learning to augment the few available skilled people can help ease this. What’s exciting about the state of the industry right now is that recent advances in Machine Learning methods are poised to make their way into deployable products,” said Nicko van Someren, Chief Technology Officer at Absolute Software.

Conclusion

AI and machine learning are redefining every aspect of cybersecurity today. From improving organizations’ ability to anticipate and thwart breaches, protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to making passwords obsolete, AI and machine learning are essential to securing the perimeters of any business.  One of the most vulnerable and fastest-growing threat surfaces are mobile phones. The two recent research reports from MobileIronSay Goodbye to Passwords (4 pp., PDF, opt-in) in collaboration with IDG, and Passwordless Authentication: Bridging the Gap Between High-Security and Low-Friction Identity Management (34 pp., PDF, opt-in) by Enterprise Management Associates (EMA) provide fascinating insights into the passwordless future. They reflect and quantify how ready enterprises are to abandon passwords for more proven authentication techniques including biometrics and mobile-centric Zero Trust Security platform.

10 Charts That Will Change Your Perspective Of AI In Marketing

 

  • Top-performing companies are more than twice as likely to be using AI for marketing (28% vs. 12%) according to Adobe’s latest Digital Intelligence Briefing.
  • Retailers are investing $5.9B this year in AI-based marketing and customer service solutions to improve shoppers’ buying experiences according to IDC.
  • Financial Services marketers lead all other industries in AI application adoption, with 37% currently using them today.
  • Sales and Marketing teams most often collaborate using Configure-Price-Quote (CPQ) and Marketing Automation AI-based applications, with sales leaders predicting AI adoption will increase 155% across sales teams in two years.

Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals. Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate.

The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The best CMOs balance the quant-intensive nature of running marketing with qualitative factors that make a company’s brand and customer experience unique. With greater insight into how prospects make decisions when, where, and how to buy, CMOs are bringing a new level of intensity into driving outcomes. An example of this can be seen from the recent Forbes Insights and Quantcast research, Lessons of 21st-Century Brands Modern Brands & AI Report (17 pp., PDF, free, opt-in). The study found that AI enables marketers to increase sales (52%), increase in customer retention (51%), and succeed at new product launches (49%). AI is making solid contributions to improving lead quality, persona development, segmentation, pricing, and service.

The following ten charts provide insights into how AI is transforming marketing:

  • 21% of sales leaders rely on AI-based applications today, with the majority collaborating with marketing teams sharing these applications. Sales leaders predict that their use of AI will increase 155% in the next two years. Sales leaders predict AI will reach critical mass by 2020 when 54% expect to be using these technologies. Marketing and sales are relying on AI-based marketing automation, configure-price-quote (CPQ), and intelligent selling systems to increase revenue and profit growth significantly in the next two years. Source: Salesforce Research, State of Sales, 3rd edition. (58 pp., PDF, free, opt-in).

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service is the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

  • 22% of marketers currently are using AI-based applications with an additional 57% planning to use in the next two years. There are nine dominant use cases marketers are concentrating on today, ranging from personalized channel experiences to programmatic advertising and media buying to predictive customer journeys and real-time next best offers. Source: Salesforce’s State of Marketing Study, 5th edition

  • Content personalization and predictive analytics from customer insights are the two areas CMOs most prioritize AI spending today. The CMO study found that B2B service companies are the top user of AI for content personalization (62.2%) and B2B product companies use AI for augmented and virtual reality, facial recognition and visual search more than any other business types. Source: CMOs’ Top Uses For AI: Personalization and Predictive Analytics. Marketing Charts. March 14, 2019

  • Personalizing the overall customer journey and driving next-best offers in real-time are the two most common ways marketing leaders are using AI today, according to Salesforce. Improving customer segmentation, improving advertising and media buying, and personalizing channel experiences are the next fastest-growing areas of AI adoption in marketing today. Source: Salesforce’s State of Marketing Study, 5th edition

  • 81% of marketers are either planning to or are using AI in audience targeting this year. 80% are currently using or planning to use AI for audience segmentation. EConsultancy’s study found marketers are enthusiastic about AI’s potential to increase marketing effectiveness and track progress. 88% of marketers interviewed say AI will enable them t be more effective in getting to their goals. Source: Dream vs. Reality: The State of Consumer First and Omnichannel Marketing. EConsultancy (36 pp., PDF, free, no opt-in).

  • Over 41% of marketers say AI is enabling them to generate higher revenues from e-mail marketing. They also see an over 13% improvement in click-thru rates and 7.64% improvement in open rates. Source: 4 Positive Effects of AI Use in Email Marketing, Statista (infographic), March 1, 2019.

Additional data sources on AI’s use in Marketing:

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

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

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

AI: The Next Generation Of Marketing Driving Competitive Advantage Throughout The Customer Life Cycle, Forrester Consulting. February 2017 (10 pp., PDF, free, no opt-in).

Artificial Intelligence for Marketing (complete book) (361 pp., PDF, free, no opt-in)

Artificial Intelligence Roundup, eMarketer, May 2018 (15 pp., PDF, free, no opt-in)

Digital Intelligence Briefing, Adobe, 2018 (43 pp., PDF, free, no opt-in).

How 28 Brands Are Using AI to Enhance Their Marketing [Infographic], Impact Blog

How AI Is Changing Sales, Harvard Business Review, July 30, 2018

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

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

IDC MarketScape: Worldwide Artificial Intelligence in Enterprise Marketing Clouds 2017 Vendor Assessment, (11 pp., PDF, free, no opt-in.)

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

Leading reasons to use artificial intelligence (AI) for marketing personalization according to industry professionals worldwide in 2018, Statista.

Lessons of 21st-Century Brands Modern Brands & AI Report, Forbes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

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

Share of marketing and agency professionals who are comfortable with AI-enabled technology automated handling of their campaigns in the United States as of June 2018, Statista.  

The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

Visualizing the uses and potential impact of AI and other analytics, McKinsey Global Institute, April 2018.  Interactive page based on Tableau data set can be found here.

What really matters in B2B dynamic pricing, McKinsey & Company, October 2018

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

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

What’s Next For You? How AI Is Transforming Talent Management

Bottom Line: Taking on the talent crisis with greater intelligence and insight, delivering a consistently excellent candidate experience, and making diversity and inclusion a part of their DNA differentiates growing businesses who are attracting and retaining employees. The book What’s Next For You? by Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, President of eightfold.ai provide valuable insights and a data-driven roadmap of how AI is helping to solve the talent crisis for any business.

The Talent Crisis Is Real

  • 78% of CEOs and Chief Human Resource Officers (CHROs) say talent programs are important, with 56% say their current programs are ineffective.
  • 83% of employees want a new job yet only 53% want to leave for a new company.
  • 57% of employees say diversity and inclusion initiatives aren’t working, and 40% say their companies lack qualified diverse talent.
  • Nearly 50% of an organizations’ top talent will leave their jobs in the first two years of being hired.
  • 28% of open positions today won’t be filled in the next 12 months.

The above findings are just a sample of the depth of data-driven content and roadmap the book What’s Next For You? delivers. Co-authors Ashutosh Garg’s and Kamal Ahluwalia’s expertise in applying AI and machine learning to talent management problems with a strong data-first mindset is evident throughout the book. What makes the book noteworthy is how the authors write from the heart first with empathy for applicants and hiring managers, supporting key points with data. The empathetic, data-driven tone of the book makes the talent crisis relatable while also illustrating how AI can help any business make better talent management decisions.

“Businesses are having to adapt to technology changes and changes in customer expectations roughly every 10 years – a timeframe that is continuing to shrink. As a result, business leaders need to really focus on rethinking their business strategy and the associated talent strategy, so they have the organizational capability to transform and capitalize on the inevitable technology shifts,” writes John Thompson, Venture Partner, Lightspeed Venture Partners and Chairman of the Board at Microsoft in the forward.

The book cites talent management researchers and experts who say “our current knowledge base has a half-life of about two years, and the speed of technology is outperforming us as humans because of what it can do quickly and effectively“ (p.64). John Thompson’s observations in the forward that the time available for adapting to change is shrinking is a unifying thread that ties this book together. One of the most convincing is the fact that using today’s Applicant Tracking Systems (ATS) and hiring processes prone to biases, there’s a 30% chance a new hire will not make it through their first year. If the new hire is a cloud computing professional, this equates to a median salary of $146,350 and taking best-case 46 days to find their replacement. The cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. Any manager who has lost a new hire within a year can relate to how real the talent crisis is and how urgent it is to solve it.

The Half-Life Of Skills Is Shrinking Fast

The most compelling chapter of the book illustrates how today’s talent crisis can be solved by taking an AI-enabled approach to every aspect of talent management. Chapter 4, The Half-Life Of Skills Is Shrinking Fast, delves into how AI can find candidates who can unlearn old concepts, and quickly master new ones. The book calls out this attribute of any potential new hire as being essential for them to adapt.  Using higher quality data than is available in traditional ATS systems, the authors illustrate how AI-based systems can be used for evaluating both the potential and experiences of applicants to match them with positions they will excel in. The authors make a convincing argument that AI can increase the probability of new candidate success. They cite a well-known Leadership IQ statistic of 46% of all new employee hires failing to adapt within 18 months, and the Harvard Business Review study finding between 40% to 60% of new upper management hires fail within 18 months. The authors contend that even Leonardo Da Vinci, one of the primary architects of the Renaissance, would have trouble finding work using a traditional resume entered into an ATS system today because his exceptional capabilities and potential would have never been discovered. When our existing process of recruiting is based on practices over 500 years old, as this copy of Leonardo Da Vinci’s resume illustrates, it’s time to put AI to work matching peoples’ potential with unique position requirements.

When Employees Achieve Their Potential, Companies Do Too   

Attracting the highest potential employees possible and retaining them is the cornerstone of any digital business’ growth strategy today and in the future. The book addresses the roadblocks companies face in attaining that goal, with bias being one of the strongest. “For example, McKinsey & Co., a top consulting agency, studied over 1,000 companies across 12 countries and found that firms in the top quartile of gender diversity were a fifth more likely to have above-average profits than those in the bottom quartile,” (p. 105). Further, “diverse executive boards generate better financial returns, and gender-diverse teams are more creative, more productive and more confident.” (p. 105).

In conclusion, consider this book a roadmap of how hiring and talent management can change for the better based on AI. The authors successfully illustrate how combining talent, personalization at scale, and machine learning can help employees achieve their potential, enabling companies to achieve theirs in the process.

Indeed’s 10 Most Popular AI & Machine Learning Jobs This Year

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019.
  • Machine Learning and Deep Learning Engineers are the most popular jobs posted on Indeed between 2018 and 2019.
  • Machine Learning Engineers are earning an average salary of $142,858.57 in 2019 based on an analysis of all open positions on Indeed.
  • Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019

These and many other insights are from Indeed’s recent report of the top 10 AI Jobs, and Salaries. Indeed’s analytics team completed an analysis of AI and machine learning hiring trends in 2019 to discover the top positions, highest salaries, and where the best opportunities are. The following are key insights from their latest study of AI and machine learning recruiting and hiring trends:

  • Machine Learning Engineers earn an average salary of $142,858.57 in 2019 based on an analysis of all open positions on Indeed. The Indeed analytics team found that the average annual salary for Machine Learning Engineers has grown by $8,409 in just a year, increasing 5.8%. Algorithm engineer’s average annual salary rose to $109,313 this year, an increase of $5,201, or 5%. Both salary bumps are likely a result of organizations’ spending more to attract talent to these crucial roles in a competitive AI job market

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • Machine Learning and Deep Learning Engineers are the most sought-after, popular jobs posted on Indeed between 2018 and 2019.  The Indeed analytics team identified the top 10 positions with the highest percentage of job descriptions that include the keywords “artificial intelligence” or “machine learning.” New jobs appearing on the list for the first time include Senior Data Scientist, Junior Data Scientist, Developer Consultant, Director of Data Science, and Lead Data Scientist. The inclusion of five new titles and the mix of skills shown in the table below reflects organizations’ growing expertise using AI, deep learning, and machine learning to drive business outcomes.

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019.  Indeed found the increase is significantly less than it was for the previous two years. During the same period, May 2017 to May 2018 AI job postings on Indeed rose 57.91%, and a whopping 136.29% between May 2016 and May 2017.
  • Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019. In comparison, searches increased 32% between May 2017 and May 2018 and 49.1% between May 2016 and May 2017. There are demand-and supply-side explanations for the 14.5% drop. From the demand side, the effects of AI and machine learning reaching broader adoption and maturing in organizations is leading to a greater variety of skills being recruited for. The 14.5% reduction reflects the broadening base of skills enterprises need to get the most out of AI and machine learning. From a supply side, potential job candidates are seeing the broadening base of skills they need to get hired, which are quickly making job descriptions from two years ago or longer obsolete. Finding candidates who have capabilities and potential to excel in AI and machine learning positions needs to get beyond just relying on job descriptions. Eightfold is doing just that by relying on machine learning algorithms to match candidates who have the optimal set of capabilities and potential for every open position an organization has.
  • New York, San Francisco, and Washington D.C. are the top three cities for AI and machine learning jobs in 2019. Indeed’s 2018 study also found New York and San Francisco leading all other metropolitan areas in open positions. New York’s diverse industries that range from banking, financial services, institutional investing, insurance to a growing AI startup community all contribute to its ranking first in the U.S. for AI positions.

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

Salesforce Now Has Over 19% Of The CRM Market

 

  • Salesforce dominated the worldwide CRM market with a 19.5% market share in 2018, over double its nearest rival, SAP, at 8.3% share.
  • Worldwide spending on customer experience and relationship management (CRM) software grew 15.6% to reach $48.2B in 2018.
  • 72.9% of CRM spending was on software as a service (SaaS) in 2018, which is expected to grow to 75% of total CRM software spending in 2019.
  • Worldwide enterprise application software revenue totaled more than $193.6B in 2018, a 12.5% increase from 2017 revenue of $172.1B. CRM made up nearly 25% of the entire enterprise software revenue market.

CRM remains the largest and fastest growing enterprise software category today according to the latest market sizing, and market share research Gartner published this weekGartner defines CRM as providing the functionality to companies across the four segments of customer service and support, digital commerce, marketing, and sales. All four subsegments of the CRM market grew by more than 13.7%, with marketing emerging as the fastest growing segment, increasing by 18.8% and representing more than 25% of the entire CRM market. Customer service and support retain its No. 1 position, contributing 35.7% of CRM market revenue, attaining $17.1B in revenues in 2018.

Key insights include the following:

  • With 19.5% market share, Salesforce has over 2X the CRM sales SAP has and over 3X of Oracle. Salesforce continues to dominate CRM globally, increasing its market share from 18.3% in 2017 to 19.5% in 2018. Adobe is the only other vendor to grow its market share in 2018. Microsoft and SAP successfully held onto to market share while Oracle lost share.

  • Adobe and Salesforce grew faster than the overall market, increasing CRM revenues 21.7% and 23.2% respectively. Adobe’s CRM sales jumped from $2B in 2017 to $2.4B in 2018. Salesforce CRM revenues increased from $7.6B in 2017 to $9.4B in 2018, growing the fastest of all competitors in this market. SAP grew 15.5% between 2017 and 2018, just below the overall market growth of 15.6%. Microsoft (15%) and Oracle (7.1%) grew slower than the market. The following graphic compares growth rates between 2017 and 2018.

  • Adobe dominates the marketing subsegment of CRM with 19% market share in 2018. Salesforce has 11.7% of the marketing subsegment, followed by IBM (5.7%), SAP (4%), Oracle (3.6%) and HubSpot (3.4%). Gartner estimates the marketing subsegment was a $12.2B market in 2018, increasing from $10.3B in 2017, achieving 18.8% growth in just a year.
  • Eastern and Western Europe were the fastest growing regions at 19.7% and 17.5% respectively. North America and Western Europe were the largest two regions with North America growing at 15.2% to reach $28.1B in revenue.

Sources:

Gartner Says Worldwide Customer Experience and Relationship Management Software Market Grew 15.6% in 2018

Market Share: Customer Experience and Relationship Management, Worldwide, 2018 (client access required)

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