Fraud detection algorithms in the insurance industry
Element AI Element AI
August 6 4 min

Fraud detection algorithms in the insurance industry

As long as there has been insurance, there have been insurance fraudsters trying to exploit the industry by making false or inflated claims and profiting from the pay-outs. And apparently the problem is growing, which means there is an increasing negative impact on insurers’ profits – as well as customers’ premiums, which must inevitably share some of the financial fallout. But businesses are now fighting back using new technology: fraud detection algorithms.

The digital detective

Fraud detection is like any other kind of detective work. Using your knowledge of what legitimate claims look like versus fraudulent ones, you’re able to discern a suspicious claim. Then you can gather further information, analyse it and make your decision.

In the world of technology and mathematics, this kind of work is referred to as data analytics. And, because artificial intelligence can process vast quantities of data quickly, with a great degree of sophistication, it’s a perfect assistant for the job. It’s able to quickly and precisely analyse and compare vast datasets, notice patterns in fraudulent claims and spot red flags, marking them out for human investigators. AI can do all this using its fraud detection algorithms.

Spotting the red flags via a fraud detection algorithm

A fraud detection algorithm uses many of the same warning signs to spot a suspicious claim as a human worker does, but it is able to leverage far more information at a faster rate - making judgements based on datasets that are simply too large for humans to work with.

Here are some of the key areas to find red flags in a claim:

  • Referral to the Special Investigations Unit (SIU). If past claims from a customer have been referred to the SIU, which is the insurer’s fraud investigation department, the AI will spot patterns in the circumstances of these referrals – for instance, suspicious coincidences in the details of the claims. Multiple incidences of referral, along with any recurring characteristics, will be flagged for investigators.
  • Previous denied claims. Once again, advanced pattern recognition is used to analyse any prior claims from a customer that have been denied, find the similarities and flag a possible pattern of fraudulent activity. A data mining technique called cluster analysis discerns high-frequency clusters of denied claims around particular account holders, addresses, phone numbers, email addresses and more.
  • Claimant’s network of associations. As in many criminal enterprises, fraudsters often operate in groups. If a person or group, previously suspected of suspicious activity, appears in relation to new claim in any capacity, AI will automatically recognise them and trigger a red flag.
  • Text mining. In the above examples, the algorithm works with “structured” data – data that’s been pre-categorized, so the AI immediately knows what it is and how to deal with it. However, text-mining deals with raw, unstructured text data, applying techniques such as natural language processing and decision logic to look for facts and relationships. Intelligence sources for analysis can range from digital claims reports and correspondence to handwritten documents and even phone call audio transcribed via voice recognition.

A rapidly expanding field

According to a 2019 study, the insurance fraud detection market will be worth as much as $7.9 billion (US) by 2024. This comes as a direct result of insurers’ growing adoption of cloud services and the increasing amount, availability and usefulness of cloud-based data for their fraud investigations.

As insurance industry AIs and their fraud detection algorithms become more sophisticated and able to access more and more federated and governed data sources, they have the potential to reach a far greater degree of accuracy and autonomy in their investigations.

The pace of advancement is being driven by an industry hungry to reduce fraud and its impact on profits and premiums, which is evident in the Connected Claims report from its 2019 summit. 71% of those surveyed said that AI and machine learning was one of the top technologies that would transform claims over the following two to five years. Furthermore, over half of the respondents said that they wanted to increase their technology investment in 2019.

This makes a compelling case for the necessity of adoption – that insurers must not only harness technology-driven fraud detection today but be ready to keep up with the rest of the pack when even greater benefits are available in the not-so-distant future.