Motor Insurance Risk Scoring Using Machine Learning

Abstract

In response to the persisting use of traditional underwriting methods in Sri Lankan insurance companies and the mounting challenges posed by delays in customer payments exacerbated by the recent economic crisis, this study endeavors to harness the power of machine learning to enhance business operations within the Sri Lankan insurance sector. While insurance firms globally have been quick to adopt automated underwriting processes driven by machine learning, Sri Lankan counterparts lag behind. The primary objective of this research is to bridge this gap by developing a sophisticated machine learning model capable of predicting risk scores for new insurance customers. By doing so, the study aspires to provide a more accurate risk assessment tool, which, in turn, promises to significantly bolster the overall efficiency and effectiveness of the insurance operations in Sri Lanka. This innovative approach aims not only to mitigate customer-related risks but also to streamline the underwriting process, thus offering a potential solution to the financial challenges imposed by the economic downturn. By embracing machine learning, Sri Lankan insurance companies can potentially navigate the turbulent waters of the current economic climate more effectively, ultimately safeguarding their business operations and improving customer experiences.