This is well known as an insurance segment in which fraud is particularly common.
Both approaches yielded promising results, indicating that AI in the form of machine learning is generally capable of identifying fraudulent claims, at minimum those that had been detected in the past.
In a second step, the authors then conducted a field experiment with an insurance company to examine the even more intriguing question of whether machine learning can identify previously undetected fraud patterns.
To this end, the insurance companies’ fraud experts (real people) investigated past claims that had been categorised as non-fraudulent by the existing (non-machine-learning) fraud detection mechanisms of the insurance company, but had nonetheless been marked as anomalous by the supervised and unsupervised machine-learning algorithms.
Interestingly, both supervised and unsupervised machine-learning algorithms managed to find a substantial number of claims that had not been investigated before settlement, but were judged as highly suspicious by the fraud experts after reinvestigation by the insurance company.
A powerful incentive
The finding that AI is able not only to detect insurance fraud but also to detect fraudulent claims that have been overlooked by existing, more conventional fraud detection mechanisms, thus certainly provides a powerful incentive for insurance companies to make use of modern AI systems.
A closer look at the identified highly suspicious claims then revealed that the claims identified by unsupervised machine learning differed to a meaningful extent from those that had been identified by supervised machine learning.
Debener and his co-authors suspect that supervised machine learning detects known fraud patterns more rigorously than the existing more conventional fraud detection mechanisms, so that these claims have a lower chance of slipping through, but unsupervised learning can go further and even detect previously unknown fraud patterns, which is a major additional benefit.
In short, the investigation suggests supervised and unsupervised machine learning algorithms act as complements rather than substitutes, and a combination should be considered for detecting insurance fraud.
The two together may well usher in a new era of fraud detection, which should provide a range of benefits for the industry and its legitimate clients.
Brian Bloch is a freelance journalist based in Germany
