With the worsening cost-of-living crisis, evidence is growing that consumers are finding it increasingly tempting to try to recoup savings on items such as insurance, in ways that are not always legitimate. This includes 'tweaking' key rating factors when generating insurance quotes online in a bid to obtain a favourable premium. While this has long been a feature of online trading, there is more that insurance providers can be doing to detect application fraud before it happens explains Nick Jackson, partnerships director at insurtech CDL.

A recent survey of Insurance Fraud Investigators Group (IFIG) members found that more than half said that they have witnessed an uptick in insurance fraud in the past year, with nearly all expecting this volume to further increase as a result of the cost-of-living crisis. Allianz's latest figures showed an 11.2% increase in fraud in 2022, equating to £7.1 million, and while traditional motor fraud, such as cash for crash scams, is down, application fraud is on the up.

Part of the problem is quote manipulation or misrepresentation, where consumers generate multiple insurance quotes, often through different channels, with key risk details, such as occupation or annual mileage, amended between quote requests until they obtain what they believe to be a more acceptable price.

Fortunately, technology is now providing a real-time solution to this problem in the form of the latest generation of ultra-fast data intelligence solutions.

These are capable of conducting a range of complex searches and analysing millions of records sub-second to deliver insight and inform actions in real time. Using this technology, it is possible to highlight when people are generating multiple quotes while making changes to key risk factors, and implement actions to stop fraud before it happens.

CDL has brought together a number of major UK insurance brands to pool data and share insights garnered using its Hummingbird data intelligence solution.

Data processed by the Hummingbird Syndicate solution is helping to understand consumer behaviours associated with quote manipulation. With this knowledge, it's possible to provide some tips for using data insights to tackle what has become an increasingly challenging industry issue.

When attempting to detect quote manipulation, sophisticated consumer matching routines are vital, as people frequently adjust personal details, such as their name, address or age, to reduce premiums and, sometimes, to mask their identity whilst experimenting with application details. Combining insights with other sources, such as MOT history or credit reference data, can assist in verifying the accuracy of declared annual mileage or driver identity.

By identifying multiple quotes involving different lead drivers, it also becomes possible to highlight potential instances of 'fronting', where quotes are requested in different names, in an effort to obtain the lowest price.

Visualisation tools also make it easier to spot suspicious patterns of behaviour. Geo-mapping, for example, can help where people declare they keep their vehicle overnight at a different location to their contact address. This analysis has highlighted unusually high volumes of quote requests involving remote areas, often considerable distances away, that are typically low rated for insurance purposes.

Using insights garnered by Hummingbird Syndicate, members set their own parameters for what constitutes suspicious behaviour. To reduce false positives, these typically factor in the type, extent and combination of changes made.

Finally, it's necessary to determine the appropriate course of action in the event of suspicious behaviour. In extreme cases, members may decline to quote altogether. However, they are also actively using insights to enable more focused customer conversations, helping to ensure the correct risk is covered for the right premium and protecting consumers who otherwise risk their policies being later invalidated at point of claim due to misrepresentation.

Customers using the solution are realising a range of benefits, including reductions in post-sale cancellations, up to 50% savings in post-sale service costs and reductions of up to 40% in post-sale chase communications, resulting from upfront automated checks, reducing bad debt levels and operational costs, as well as improving experiences for genuine consumers. They also gain significant economies of scale by sharing supporting infrastructure, delivering a cost efficient solution.

By avoiding 'toxic' risks, members are demonstrating to their insurer partners that they are taking action to develop better quality business and, in return, are able to negotiate more competitive deals and drive growth in an extremely price-driven sector.