Hybrid Machine Learning Algorithms for Risk Assessment in Insurance Industry: Empirical Review

Abstract

The rising usage of machine learning algorithms in the insurance sector has had a substantial impact on risk assessments. These models anticipate loss probability accurately and help with business strategy. Hybrid machine learning algorithms, which mix different methodologies, are frequently used to improve performance. Combining supervised and unsupervised algorithms makes it easier to identify hidden patterns in data and establish correlations between the risk factors and outcomes. However, challenges remain in the insurance industry's full acceptance of this technique. This study examined 10 research articles published between 2018 and 2023, with an emphasis on the application of hybrid machine learning algorithms for risk assessment in insurance. The articles were picked for their relevance, quality, and accessibility. The findings suggest individual methods are frequently outperformed by hybrid approaches that mix different algorithms or use resampling techniques. The study also highlights several challenges, such as dealing with imbalanced datasets, selecting appropriate feature selection methods, correcting class imbalance, assuring model interpretability, generalizability, ensuring data accuracy, privacy, and availability, algorithm complexity, data volume requirements, and multicollinearity in specific datasets. This article concludes by emphasizing the importance of developing novel approaches for dealing with imbalanced datasets, selecting appropriate feature selection methods, and improving model interpretability, while also investigating how the use of newer approaches such as deep learning and ensemble methods could improve risk prediction accuracy even further.

Keywords: Hybrid Machine Learning, Machine Learning, Risk Assessment, Insurance Industry, Risk Prediction