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AI and Fraud Prevention: Part 2

What is it about AI that can cut through the fog of complex fraud prevention?

It “sees” specific bad behavioral patterns more clearly than traditional methods.

To many fraud fighters, the promises of technology can often feel like an echo in a cavern—loud, persistent, but hollow. And among these echoes, AI-Enhanced machine learning (ML) is often hailed as the ‘next big thing’ to revolutionize fraud detection and prevention. But the skepticism is understandable, as the question arises: What can ML truly provide that isn’t already in our toolbox?

Let us consider ML not as a panacea, but as a process of finely tuning a radio. We need to construct a model (the equivalent of building a radio) which needs to be based around our business problem (akin to selecting a radio station), and we must continuously refine the model to solve the problem (like tuning into the exact frequency)

The trio of software, models, and hardware that form ML offers a new lens for analyzing complex data. It allows for the integration of expansive datasets and distills the parameters that matter in specific situations, helping cut through irrelevant noise. Through algorithmic analysis, we can discern causality from mere coincidence, creating a predictive model to anticipate future behavior.

Importantly, leveraging ML is not just about the technology. It is also about coupling it with a deep understanding of the business domain to ensure that the technology is being used to pinpoint and address the right problems. ML does not replace professionals but empowers them, enhancing capabilities and shedding light on fraudulent patterns obscured by the data fog.

One trap we need to sidestep is the overemphasis on technology alone. To fully utilize ML, a more integrative, iterative approach is beneficial. This approach entails piloting, learning, and refining to exploit the potential of technology effectively.

ML presents the tools to sift through expansive datasets, map out significant patterns, and build dependable predictive models. It equips us to navigate through the complex fog of data and detect subtle patterns that could indicate fraudulent activity. With such tools we raise fewer but more pertinent red flags and avoid those dreaded false positives.

While ML holds promise, it requires a balanced approach. It is not a standalone solution but a tool that, when meticulously utilized and refined, can bolster your capabilities in fraud detection and prevention.

AI enhanced machine learning’s value lies not just in its technology, but also in its application and integration with existing knowledge and expertise. Consider AI and machine learning as an ally in your fight against fraud—not as a silver bullet, but as a powerful tool that requires careful tuning. Once fine-tuned, it serves as a clear beacon amidst the static, aiding in unmasking the fraudsters hiding in our systems.

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Author: John Bethell

This article was co-authored: PETER TAYLOR is an Accredited Counter Fraud Specialist with a successful career on the fraud side of Loss Adjusting having been the Head of Fraud for major loss adjusters. He has pioneered the benefits of intervention on suspect claims, the introduction of conversation management for desktop investigations, and the benefits of technology for intelligence led investigations. He set up his own consultancy business in 2012 and has widened the scope of his experience from claims investigations to include online retail, banking, credit providers and local government. JOHN BETHELL has a long term background in Consultancy ( Booz Allen Hamilton) Financial services ( HSBC, AXA ) and Technology ( FatBrain, Autonomy, AOL), He has strong interest in AI-Enhanced Machine learning ( ML) having spent the last four years working with a proven US AI provider focused on augmenting C- Suite decision making in FS in such diverse areas as Fraud, AML, Insurance, Underwriting, Trading and Investment. He is strongly aware of how ML technology can be applied in settings outside of FS.