Aviva recently confirmed it had witnessed 51,000 instances of fraud at policy application last year, nearly two-thirds (64%) up on 2022 [i]. A major concern for the market is the rise in synthetic fraud, where fraudsters manipulate identities or manufacture new identities that do not associate with a real person, often evading third-party fraud solutions. In fact, as much as 85% of synthetic identities can be missed by third-party fraud models.
Detecting application fraud requires a careful balance between focusing effort on catching the bad guys, and risking upsetting the genuine customer with clunky, time-consuming identity checks. Living in an age of Amazon and Uber, customers expect instant gratification and the perfect customer experience, and the low frequency of customer touchpoints in insurance means that it is vital to get every opportunity right.
Fortunately, accurate pertinent data has now been coupled with the right technology to red flag the signs of fraud without affecting the honest customer’s experience. In fact, the best fraud detection solutions can even enhance that journey, while allowing fraud teams to prioritise investigations with those customers flagged as posing a risk.
A great example of such technology is LexisNexis® Emailage® Rapid, which uses the customer’s email address and other personal information provided during the application to score the insurance application for the risk of fraud, including synthetic fraud. This calls on a network comprising of 5.9 billion digital identifiers, 30 million confirmed fraud events, and an average of over 82 thousand fraud events shared daily.
With vulnerable people falling victim to ghost broking, the City of London Police[ii] is urging people to be wary of cheap motor insurance deals on social media and via messaging apps. Ghost brokers will often supply their own email address for documentation from an insurance provider, so using data enrichment solutions to investigate email addresses and their digital footprints early in the application process is not just sensible – it’s essential.
Quote manipulation is another application fraud tactic that is hitting the insurance industry hard. Analysis has shown that 48%[iii] of motor insurance buyers thought it was completely or somewhat acceptable to deliberately misstate information to obtain a lower quote. In response, LexisNexis® Quote Intelligence uses shopping behaviour data to help identify whether information for a quote is being manipulated to get a cheaper price, or for policy ‘fronting’.
Details of the vehicle itself (like MOT and mileage) can also be verified for potential misstatements by an applicant, using solutions like LexisNexis® Vehicle Insights.
The frequency of claims themselves can often raise suspicion. This is where knowledge of the claiming history of an individual as well as the asset can be so valuable. For example, if an insurance provider can see the claims from a property from further back than when the current claimant lived there, they might be able to build a better understanding of whether the new claim is fraudulent. In motor, historical claims data might reveal whether a car has had a history of claims behind it. Soon, our cross-market contributory claims database, LexisNexis® Precision Claims, will be available to use at quote to help highlight fraud risks and support underwriting decisions.
Disrupting fraud is an ongoing challenge, but stopping fraudsters at the front door by identifying application fraud can be highly effective. With so many data solutions in their toolbox, insurance providers can go a long way to keep fraudsters out in the cold.
[i] https://www.aviva.com/newsroom/news-releases/2024/06/aviva-detected-39-percent-more-claims-fraud-in-2023/
[ii] https://www.cityoflondon.police.uk/news/city-of-london/news/2024/january/ghost-broker-who-targeted-sudanese-community-sentenced/
[iii] https://risk.lexisnexis.co.uk/about-us/press-room/press-release/20220511-quote-intelligence
Content provided by Luke Owens, product manager, UK and Ireland, LexisNexis Risk Solutions, Insurance.
