Enhancing financial services via data analytics and machine learning

Abstract

The field of data analysis and machine learning has steadily advanced since the 1990s, gaining widespread industrial use particularly in the early 2000s. However, the financial sector has been relatively conservative and slow in adopting these advancements. In recent years, there has been a growing demand in the financial market to embrace various forms of analysis using data analytics and machine learning. This thesis aims to address several real-world challenges within the industry through the application of data analysis and machine learning. This study addresses three key challenges in the industry by leveraging data analysis and machine learning to diagnose and determine customer risk aversion in financial investments, exploring the potential of recommendation systems within the insurance sector, and predicting early repayment rates in mortgage companies.