Supervised Learning Algorithms for Non-Life SCR Ratio Forecasting

Marius Acatrinei, Adriana AnaMaria Davidescu, Laurentiu Paul Baranga, Razvan Gabriel Hapau and George Calin
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Marius Acatrinei: Institute for Economic Forecasting, Romanian Academy, Bucharest, Romania
Adriana AnaMaria Davidescu: Bucharest University of Economic Studies, Bucharest, Romania
Laurentiu Paul Baranga: Bucharest University of Economic Studies, Bucharest, Romania
Razvan Gabriel Hapau: West University of Timisoara, Timisoara, Romania
George Calin: Bucharest University of Economic Studies, Bucharest, Romania

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES, 2024, vol. 6, issue 1, 631-647

Abstract: The solvency is measured by the Solvency Capital Requirement (SCR). This study seeks to determine the best financial ratios to forecast SCR because it is significant. There is seasonality, data jumps, and shifts in insurance indicators, which make prediction of SCR difficult. Different machine learning algorithms are applied to the insurance market in this research to see how well they can describe and predict the SCR ratio. Gaussian process regression, ensemble methods, regression decision trees, stepwise regression, and neural networks were used as supervised learning techniques to find the most suitable method to predict SCR. According to our analysis of nonlife insurance data from Romania between 2016-2020, debt ratio, reserve adequacy, receivables, and liquidity are among the key indicators that should be considered when forecasting SCR. These findings can be useful for policymakers, regulators, actuaries, and professionals involved in risk management or the insurance industry.

Keywords: general insurance; machine learning; risk prediction; solvability capital requirement ratio. (search for similar items in EconPapers)
JEL-codes: G22 G28 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rom:conase:v:6:y:2024:i:1:p:631-647

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