Damien Rourke, partner, Clyde & Co
How can you tell when someone is lying? Is it their body language that gives them away? Their eye movements; their gestures; the way they touch their face whilst speaking? Or is it the nature of the lie itself? Not enough detail or perhaps too much detail?
The truth is, unless you’re a skilled practitioner, catching someone out in a lie can be difficult. That’s something the insurance industry has learnt the hard way. In 2022, the sector detected 72,600 fraudulent insurance claims, according to the Association of British Insurers (ABI). The actual number will be much higher, as many go undetected.
Insurers have made many attempts to root out these fraudulent claims. Voice stress analysis was much heralded in the early 2000s. Israel developed a technology that listened to a claimant’s voice during a phone call to detect abnormally high levels of stress. Cognitive interviewing was a psychological approach involving skilled call handlers interviewing claimants using a gentle but persistent form of questioning developed for use with children who were the victims of sexual abuse.
Now, with the rapid development of artificial intelligence (AI), a new approach is being pioneered. The key is the inconsistency of the liar. In short, telling a detailed lie about a car accident requires imagination, a good memory, the ability to act, a degree of confidence, intelligence and flexible thinking. Telling a lie once is easy; telling it over and over again is hard. Details will change. Terminology fluctuates. Aspects become over-embellished. It’s these changes and inconsistencies that AI can take advantage of.
“In 2022, the sector detected 72,600 fraudulent insurance claims, according to the Association of British Insurers (ABI).”
Take, for example, a faked car accident in which a driver and three passengers are involved. All four people must provide statements, complete claims forms, complete medical forms, speak to physicians. The chances that all four alleged victims offer consistent statements and accounts over a period of several months is low. Human nature just isn’t like that. Our memories are not fool proof.
Those inconsistencies existed before AI; there’s nothing new about them. But what is new is AI’s ability to ingest large volumes of data, analyse it and then identify the inconsistencies. What would once have taken a claims fraud specialist an entire day to accomplish can now be achieved in minutes.
At Clyde & Co, we’ve been investing heavily in developing the capabilities of our Newton platform for several years. To identify fraud, the system must go through two key steps. First, it must be able to absorb and understand written materials – and this is where one of the big leaps has been made. Early on, the insurance sector focused on template-based optical character recognition (OCR). The system was taught that on form A, for example, the information contained in box B would always be a date or a name. That’s fine for a well-structured document, but this old system can’t understand a set of free-form doctor’s notes. The newer approach we’ve adopted is holistic O