A Big Data Framework to Address Building Sum Insured Misestimation

Callum Roberts, Adrian Gepp*, James Todd

*Corresponding author for this work

Research output: Contribution to journalArticleProfessionalpeer-review


In the insurance industry, the accumulation of complex problems and volume of data creates a large scope for actuaries to apply big data techniques to investigate and provide unique solutions for millions of policyholders. With much of the actuarial focus on traditional problems like price optimisation or improving claims management, there is an opportunity to tackle other known product inefficiencies with a data-driven approach. The purpose of this paper is to build a framework that exploits big data technologies to measure and explain Australian policyholder Sum Insured Misestimation (SIM). Big data clustering and dimension reduction techniques are leveraged to measure SIM for a national home insurance portfolio. We then design predictive and prescriptive models to explore the relationship between socioeconomic and demographic factors with SIM. Real-world results from a national home insurance portfolio provide actionable business insight on SIM and facilitate solutions for stakeholders, being government and insurers.
Original languageEnglish
Article number100396
Pages (from-to)1-13
Number of pages13
JournalBig Data Research
Publication statusPublished - 28 Aug 2023


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