Machine Learning Techniques in Financial Applications

Abstract

Over the past few years, the financial sector has witnessed an increase in the adoption of machine learning models within banking and insurance domains. Advanced analytic teams in the financial community are implementing these models regularly. This paper aims to explore the various machine learning approaches utilized in these sectors and offers recommendations for selecting suitable methods for financial applications. Additionally, the paper provides references to R packages that can be used to compute the machine learning methods. Our aim is to bring a valuable contribution to the field of financial research by providing a more comprehensive and advanced method of credit scoring, which in turn improves assessments of customers' debt repayment capabilities and improves risk management tactics.