The Assessment of Early Warning for Insurance Company Using Machine Learning Methods

Abstract

Developing an early warning indicator is essential to strengthen the financial structure and take necessary precautions in a non-life insurance company. This paper aims to implement machine learning techniques on financial ratios to estimate the capital requirement ratio and investigate the solvency of an insurance company. For this purpose, the historical ratios of insurance companies in an emerging market are considered in accordance with the regulator’s solvency requirements. The ratios collected based on the performances of insurance companies in Türkiye are studied in two cases based on the number of features to be included in to four machine learning algorithms. “Full” data set with 69 and “Boruta” implemented data set with 33 ratios are employed to depict the efficiency of methods in predicting the early warning state of the company in terms of their capital requirement ratio predictions. Additionally, the assessment of these predictions to be utilized as an early warning indicator is performed. The findings illustrate that proposed early warning model predicts well the capital requirement ratio one year in advance. Moreover, among four ML methods, XGBoost achieves a prediction accuracy of 85% for estimating the state of the solvency in an insurance company compared to the other algorithms.

Keywords

• Capital requirement ratio
• Early warning
• Solvency
• Machine learning
• Financial ratios

References

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