Advancements in technology have ushered in an era of artificial intelligence within the insurance sector. Whether deploying AI at the point of application, during claims processing, or in detecting manipulated evidence, technological innovation continues to drive progress and expand adoption. However, it is important to recognise that combating fraud requires not just cutting-edge technology but also a data-led approach.
In this article, we will delve into the efficacy of data sharing as a pivotal strategy in combating insurance fraud. Drawing from firsthand experiences engaging with insurance providers at Synectics and shedding light on the benefits of data sharing in the sector, we will also explore the pitfalls of operating in silos.
To Share or Not to Share Data?
For over three decades, sharing data has been fundamental in the fight against insurance fraud in the domestic market, most notably in personal motor insurance. The industry has demonstrated commendable collaboration and data-sharing initiatives. This is evident through the widespread adoption of services offered by organisations like the Insurance Fraud Bureau (IFB) and Synectics, which maintain a fraud data-sharing consortium called National SIRA. National SIRA alone has helped saved over £7 billion that would have been lost due to fraud.
Dangers of Working in Silos
Recent interactions with insurers across various lines of business such as Agriculture, Pet, Life & Protection, Travel, and Import Bonds; have once again highlighted a critical issue - the lack of centralised data sharing.
An example comes from the agriculture sector, where a policyholder managed to secure multiple policies with different insurers and filed numerous claims. The absence of a centralised database meant that this fraud was only discovered by coincidence.
Similarly, our Special Investigations Unit (SIU) recently dealt with a claim in the Life & Protection sector where the lack of cross-industry data sharing left critical intelligence gaps. Without access to comprehensive, shared data, identifying and addressing fraudulent claims becomes a matter of chance rather than systematic detection.
Non-motor insurers often rely excessively on good fortune or the meticulousness of individual underwriters, claims handlers, or service providers to identify suspicious activities. This reliance on human intervention rather than systematic data sharing is inefficient, risky and time consuming.
How Can Insurers Mitigate Their Risks?
These incidents underscore the urgent need for enhanced data-sharing mechanisms, drawing inspiration from the proven model employed in motor insurance. Establishing a centralised database for quotes, policies and claims, accessible in real-time, would significantly bolster fraud prevention efforts. Additionally, leveraging existing fraud data repositories would further fortify risk mitigation strategies.
While cutting-edge technology is undoubtedly crucial in combating insurance fraud, the significance of data sharing and collaboration cannot be overstated. By embracing trusted collaborative data-sharing initiatives, such as those in motor insurance, insurers across various sectors can strengthen their defences against fraudulent activities. This approach not only addresses intelligence gaps for insurers but also unlocks the potential of AI to make even further improvements in detecting fraud effectively. By providing comprehensive and accessible centralised data using AI systems fraud leaders can operate at their highest capacity, identifying and preventing fraudulent activities with greater accuracy and efficiency.
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