Fraud

I’ve been in the industry for over 20 years, traveled around the country to conferences, and listened to dozens of panels and keynotes. More times than not, the “next big thing” turns out to be a passing fad, or a shift that takes years to fully materialize. This was not the case at CONNECT 26, Q2’s annual customer conference. It became clear that the fraud industry is at an inflection point, where the questions are changing, conversations are building nuance, and decisions hinge on experience and trust. The shift from alert queues to Fraud Intelligence The biggest shift I noticed was that financial institutions are shifting from an alert-centered operational model to a foundation of Fraud Intelligence.
An alert-queue model is fairly easy to picture. After an alert is triggered, an analyst picks it up, investigates it, and closes the ticket. In this system, the process happens repeatedly and predictably. It was efficiently built for an era in which fraud was episodic, relatively slow-moving, and detectable at the transaction level. But today’s fraud attacks are different. Armed with AI, fraudsters are striking with greater frequency, targeting a broader set of financial institutions, and using sophisticated strategies to hide fraudulent payments in plain sight. The result is a barrage of flashing alerts, or a panicked customer reporting the attack long after the money has moved. Authorized payment fraud and business email compromise are showing up in the P&L now, not in some future risk model. To navigate this new era, financial institutions require a fundamentally different operating model. We call this Fraud Intelligence. It’s a behavior-centric, real-time, data-driven approach to fraud, using connected technology study patterns across the entire customer base, track how attack methods evolve, and measure success by how much loss was prevented, rather than how many alerts were closed. This is a system built for modern fraud patterns, and ready to evolve overtime.  How AI enables the shift to Fraud Intelligence Artificial intelligence makes Fraud Intelligence possible in three ways.  First, AI takes on the scale of attacks. Transaction monitoring generates an enormous amount of noise, like alerts that go nowhere and false positives that eat up analyst time. AI handles that burden. Instead of a team chasing a queue, analysts can focus on the cases that require more judgement. In return, good customers experience less friction, and fraud teams chase fewer dead ends. Second, AI changes when the threat becomes visible. By surfacing suspicious signals earlier in the fraud lifecycle, AI allows financial institutions to be proactive, rather than reactive. When early signals are combined with connected intelligence from the across the digital banking session, the result is a faster decision, grounded in fuller picture of session behavior, payment history, and back-office activity surrounding it. Third, AI enables fraud models to iteratively improve. AI’s continuous learning mechanisms—across institutions, channels, and threat types—mean the system gets smarter the more it’s used. This turns each incident into an asset rather than just a loss. Fraud Intelligence checklist for financial institutions  To strengthen fraud operations with Fraud Intelligence approach, leaders should look beyond the capabilities of point solutions, and asses their technology vendors through a broader lens: 

• How does the model perform on our specific customer base? Controlled demos are great for an introductory conversation, but they can’t answer questions about how the technology fits into real use-cases, with production evidence. Every financial institution is different, and fraud leaders should know how the model will perform for them. 
• What happens when the model drifts over time? We all know that fraud tactics are constantly evolving, so the technology needs to have an evolution plan and mitigation if the models start to drift. Strong technology vendors will have a clear plan of action. 
• What is the governance framework for retraining? The regulatory environment around fraud heightens the stakes for governance. It’s vital to understand who owns model updates, how often retraining occurs, and what guardrails are in place
• What does activation actually look like, and what is a realistic timeline value? Fraudsters aren’t waiting to attack. Understand the speed of deployment that your institution needs, and work with a technology vendor who can keep pace. 

How financial institutions are leveraging Fraud Intelligence Consider a fraud case where commercial customer’s controller falls for a phishing email.  Once credentials are compromised, and the attacker gets in and then waits. For days, they study the customer’s vendor list, payment cadence and approval flow. When the wire finally goes out — to a familiar vendor, for a plausible amount, on a Tuesday morning — nothing about it looks wrong in isolation. In an alert-centric operation, it clears. We’ve seen fraud just like this. In one case we heard about, the attacker pushed eight Automated Clearing House (ACH) batches totaling more than $3 million. Legacy fraud tools did not catch it until the following day. While some funds were recovered, more than $2 million had already moved into mule accounts and were gone. The signals were there. The system just was not built to see them. When that same data was viewed by connected technology, using a fraud intelligence approach, the outcome was categorically different. Fictitious accounts were flagged at creation, with AI built detectors finding the signals that showed the users weren’t who they were claiming to be. Within 30 seconds of the fraudulent session beginning, ACH entitlement for the fake user would have been automatically disabled and none of the eight batches would have ever been initiated.  This is the difference between an alert-queue operation and true Fraud Intelligence.  What kind of operation is your institution building? Fraudsters aren’t running isolated scams anymore. They’re running operations, complete with reconnaissance, patience, and tools that rival what most fraud teams have access to. An alert-queue model built for episodic fraud was never going to keep pace with that. Adopting Fraud Intelligence approach will allow financial institutions to stop losses before they happen, build trust with customers, and get stronger with every incident it encounters. The inflection point is here. The checklist above is a starting point — not just for evaluating vendors, but for understanding where your operation stands today and what it would take to close the gap. The post The Fraud Fight Has Reached an Inflection Point. Here’s What That Means for Your Team. appeared first on About Fraud.

Source: https://www.about-fraud.com/the-fraud-f ... your-team/