Preventing the Fraud on the Edge

The other day, I was on the phone with a fraud executive who said something that really stuck with me. He works at one of the five largest banks in North America, and when I asked about his biggest challenges he told me, “We know where 97% of the fraud vectors happen. We have the requisite tools, solutions, and intelligence to identify them. What I need is a solution that can find the 3% of fraud on the edge. That 3% is what keeps me up at night.”

I have no doubt his feelings echo those of many financial institutions. Regardless of size, any organization dealing with people’s money will be aware of the main threats. Things like AML are required for a reason, right?

But criminals are getting more sophisticated, especially with the major advancements in AI and machine learning. Fraud is possible on a larger scale than ever. On the edge of that scale, outside normal fraud prevention’s reach, is where the criminals really thrive.

Do you know where your fraud edges are?

Given our reliance on the global digital banking economy, fraud continues to be a persistent and ever-growing phenomenon and threat to consumers. As technology usage increases and permeates our daily lives and interactions with our digital accounts, banking fraud leaders need to be ever more vigilant and cognizant of not only known fraud vectors, but also of the “fraud on the edge.”

Most fraud professionals can recite their extensive legacy fraud systems and controls for the myriad fraud vectors – Account Takeover (ATO), Synthetic Identity Theft, Social Engineering Scams and Phishing, Real-Time Payments (including Zelle/Venmo/CashApp), ACH and Wire, Account Opening – that require advanced, real-time monitoring and decisioning solutions for fraud prevention, detection and mitigation.

Yet, the technical sophistication and creativity of fraudsters and organized fraud rings have caused fraud professionals to be wary of and concerned with the unknown edges of fraud committed on a daily basis.

So, is there a way to see the unseen? To find and prevent the fraud attacks seemingly coming from the darkness? Yes, there is—you just need to be sure you’re using the correct tools.

Finding fraud at the edge

The adoption of AI and Machine Learning (ML) solutions specifically designed for fraud mitigation provide proven and scalable defense layers for both known and unknown fraud. These fraud risk barriers — augmented by behavioral biometrics, digital ID verification, advanced authentication (think Multi-Factor Authentication and Tokenization) – provide real-time monitoring and 24/7 surveillance.

Further, advanced technological tools such as Unsupervised Machine Learning (UML) and not surprisingly, Generative AI, can be deployed as additional and effective components of an overall fraud, risk and security infrastructure.

Each of the top four US banks report having technology budgets exceeding $10 billion. Many of their capital investments focus on AI/ML initiatives for risk management, regulatory compliance, and mitigating payment fraud losses. To add to that, Fortune Business Insights projects the machine learning market to expand from $26B in 2023 to $225.91B in 2030.

It’s clear which tools to choose to expand your solution to catch fraud at the edge. You need to fight fire with fire and leverage both AI and ML to create a platform that catches new fraud vectors as quickly as they rise.

How DataVisor can help find the extra 3%

As leaders in real-time fraud decisioning and mitigation solutions of the banking and payments sectors, DataVisor works with some of the industry’s leading FIs, Payment Processors, and Ecommerce entities. Our core Machine Learning skills and competencies, combined with decade long experience with UML modeling for unknown fraud, enable our customers to reduce the potential losses that occur “on the edge” and outside of their known fraud controls.

Our platform’s Decision Flow capabilities offer the high queries per second (QPS) and hyper-low end to end detection latency of under 100ms. This empowers fraud teams to make decisions in real-time with confidence and protect against rising new payment fraud vectors. As our many case studies show, the platform lifts detection up to 20x, reduces false positives by as much as 40%, and can yield annual savings of more than $15 million—all with a rapid integration of under 2 weeks.

I could go on all day about what makes DataVisor the best real-time AI fraud detection and prevention solution on the market. If you’d like to hear me do just that, let’s book a time to talk.