Skip to content

Posts tagged ‘fraud detection’

How To Redefine The Future Of Fraud Prevention

How To Redefine The Future Of Fraud Prevention

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

Start By Turning Trust Into A Sales & Customer Experience Accelerator

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

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

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

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

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

An Overview of Kount’s Technology Stack

How To Redefine The Future Of Fraud Prevention

Realize Trust Is the Most Powerful Revenue Multiplier There Is

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

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

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

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

How To Redefine The Future Of Fraud Prevention

 

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

10 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.
%d bloggers like this: