Using behavioural science to reduce opportunistic insurance fraud

Tim Mitchell and Benny Cheung
Additional contact information
Tim Mitchell: Principal at Decision Technology, UK
Benny Cheung: Director at Decision Technology, UK

Applied Marketing Analytics: The Peer-Reviewed Journal, 2020, vol. 5, issue 4, 294-303

Abstract: Undetected, opportunistic fraud in the form of dishonest or exaggerated information in applications, renewal or claims processes is estimated to cost the UK insurance industry up to £1bn per year. This paper reports on research commissioned by the Insurance Fraud Bureau, in which two online randomised controlled trial experiments were run to test ways of tackling this problem. Both of the experiments involved the application of behavioural science to create short consumer-facing messages, one in an above-the-line advertising context, and the other in an online insurance claims or application context. Results showed the majority of messages to work better than controls for both experiments, improving perceptions and changing behaviour. The results have enormous implications for the insurance industry, and learnings for marketing and insights professionals more broadly. For the former, correct application of these experiments’ findings could improve industry perceptions, and significantly improve revenues by avoiding falsified applications and inflated claims. Expertise is needed to ensure the findings are applied in the right way across an insurer’s potentially multi-channel application and claims processes. For the latter, the study demonstrates the importance of context in the use of behavioural science and the need for the appropriate testing of customer communications.

Keywords: behavioural science; nudges; randomised controlled trials; operational communications; above-the-line communications; opportunistic fraud; interventions (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2020:v:5:i:4:p:294-303

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