IS ARTIFICIAL INTELLIGENCE THE ANSWER TO INSURANCE FRAUD?

• Date: 08 February 2021
• Author: Cannings, Nigel

Ongoing investment suggests that the perception amongst insurance professionals is yes. According to a 2021 survey, approximately 31% of insurance CIOs reported that they had already deployed AI and an additional 23% will deploy in the next 12 months. Focussing on Fraud and a recent survey by the Coalition Against Insurance Fraud showed that 21% of respondents are planning on investing in AI for fraud detection over the next 2 years. Yet against this backdrop of adoption, we see that most insurance companies process only 10-15% of the data they hold. The majority of the data currently analysed by insurers is structured, highly organized and formatted in a way that makes it easily searchable, housed in relational databases. Circa 10% of data is structured so whilst we can see that Insurers are making a dent on unstructured data there remains valuable insights left hidden in vast volumes of unstructured data. Effective utilisation of this unstructured data can help insurers identify potential fraud, but also shorten the customer journey by validating information more efficiently. One form of unstructured data that remains significantly underutilised is voice.

AI has an enormous remit, but it is Conversational AI, natural language processing (NLP) and automatic speech recognition (ASR) that have the potential to change the game across fraud and the contact center. Call centers are still seen as the soft target for fraud, with agents being faced with a constant barrage of sophisticated social engineering techniques, designed to manipulate and by pass scrutiny.

Contact center agents handle scores of calls a day, each one expected by business and customer alike to process queries as quickly as possible, and today agents are working remotely with the added pressures that that brings. These factors combined mean the call centre presents a vulnerability that even inexperienced fraudsters are able to exploit. Unless a caller has been previously flagged for suspicious activity, or is particularly unskilled at the social engineering requisite to scamming, their fraudulent first notification of loss (FNOL) may be processed like any other. This vulnerability is further exposed during times such as these where economic downturn correlates with an uptick in organised and opportunistic fraud. An example of this uptick is the spike in motor vehicle theft since the start of the pandemic. The NYPD reported a 57% increase in thefts of cars, motorcycles and...