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A Big Data Framework to Address Building Sum Insured Misestimation

Research output: Contribution to journal › Article › peer-review

Standard Standard

A Big Data Framework to Address Building Sum Insured Misestimation. / Roberts, Callum; Gepp, Adrian; Todd, James.
In: Big Data Research, Vol. 33, 100396, 28.08.2023.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Roberts, C, Gepp, A & Todd, J 2023, 'A Big Data Framework to Address Building Sum Insured Misestimation', Big Data Research, vol. 33, 100396. https://doi.org/10.1016/j.bdr.2023.100396

APA

Roberts, C., Gepp, A., & Todd, J. (2023). A Big Data Framework to Address Building Sum Insured Misestimation. Big Data Research, 33, Article 100396. https://doi.org/10.1016/j.bdr.2023.100396

CBE

Roberts C, Gepp A, Todd J. 2023. A Big Data Framework to Address Building Sum Insured Misestimation. Big Data Research. 33:Article 100396. https://doi.org/10.1016/j.bdr.2023.100396

MLA

Roberts, Callum, Adrian Gepp and James Todd. "A Big Data Framework to Address Building Sum Insured Misestimation". Big Data Research. 2023. 33. https://doi.org/10.1016/j.bdr.2023.100396

VancouverVancouver

Roberts C, Gepp A, Todd J. A Big Data Framework to Address Building Sum Insured Misestimation. Big Data Research. 2023 Aug 28;33:100396. Epub 2023 May 24. doi: 10.1016/j.bdr.2023.100396

Author

Roberts, Callum ; Gepp, Adrian ; Todd, James. / A Big Data Framework to Address Building Sum Insured Misestimation. In: Big Data Research. 2023 ; Vol. 33.

RIS

TY - JOUR

T1 - A Big Data Framework to Address Building Sum Insured Misestimation

AU - Roberts, Callum

AU - Gepp, Adrian

AU - Todd, James

PY - 2023/8/28

Y1 - 2023/8/28

N2 - 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.

AB - 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.

U2 - 10.1016/j.bdr.2023.100396

DO - 10.1016/j.bdr.2023.100396

M3 - Article

VL - 33

JO - Big Data Research

JF - Big Data Research

SN - 2214-5796

M1 - 100396

ER -