fraud ai

AI and Fraud Prevention: Part 3

How do we crack the conmen’s code: Harnessing the Predictive Power of AI and Machine Learning

We need to find the signals that are meaningful.   

As fraud fighters, we often find ourselves in the role of radio operators. Our main task? To decipher complex patterns – our ‘signals’ – hidden within the hum of transactional data – our ‘static’. AI enhanced machine Learning (ML) serves as an advanced tuning tool, helping us recognize these concealed signals more effectively. To raise fewer but more pertinent red flags and avoid many of those dreaded false positives. 

Just as we tune a radio to resonate with different frequencies, ML models are trained to recognize patterns within data. Each example of fraudulent and non-fraudulent behavior we feed into the ML model sets a new frequency range. Over time, the model learns to distinguish between the fraudulent and the non-fraudulent, turning a deafening wall of white noise into a symphony of meaningful patterns.                                             

But as we know, to get to that sweet harmony, we must address signal quality. Poor data quality can lead to a radio playing static-filled music, just as a machine learning model trained on incomplete or inconsistent data can lead to those dreaded false positives. To ensure optimal performance, we must emphasize precise tuning and fine-tuning of our ML models. We must commit to iterative cycles of model training, testing, and validation, analogous to the meticulous adjustments we make to our radio until we find that perfect station.

AI can harness not just about the math’s of fraud but the behaviour too.  This makes it hard for the conmen to hide. Continuing the radio analogy, it can be as hard for them to hide within the data as it is to broadcast an undetected radio station, within range of receiver tuned into their band and wavelength.  The big difference AI contributes is this point of discovery can be the start of rapid detection of many more threat actors  rather than the end of the journey.

The future of fraud detection lies in harnessing this predictive power of AI and machine learning.

Tagged with:
Posted in: ,
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.