Predictive Analytics for Credit Risk Assessment in Commercial Banking

Thesis Abstract

Abstract
The banking sector plays a crucial role in the economy by providing financial services and facilitating economic growth. One of the key functions of banks is to assess credit risk accurately to make informed lending decisions. In recent years, the use of predictive analytics has gained popularity in the banking industry for credit risk assessment. This research project focuses on the application of predictive analytics in commercial banking for credit risk assessment. The primary objective of this study is to develop a predictive analytics model that can effectively assess credit risk in commercial banking. The research begins with a comprehensive review of existing literature on predictive analytics, credit risk assessment, and their applications in the banking sector. This literature review provides a theoretical foundation for the study and highlights the current trends and challenges in the field. The research methodology chapter outlines the approach taken to collect and analyze data for the development of the predictive analytics model. The methodology includes data collection from financial institutions, preprocessing and cleaning of the data, feature selection, model development, and evaluation techniques. Various statistical and machine learning techniques are applied to build a robust predictive model that can accurately predict credit risk. The findings chapter presents the results of the analysis conducted on the data collected from commercial banks. The predictive analytics model developed in this study demonstrates high accuracy in assessing credit risk, thereby assisting banks in making more informed lending decisions. The discussion of findings chapter provides an in-depth analysis of the results, highlighting the strengths and limitations of the predictive analytics model. In conclusion, the study emphasizes the significance of predictive analytics in credit risk assessment for commercial banking. By leveraging advanced analytics techniques, banks can enhance their risk management practices, improve loan portfolio performance, and ultimately contribute to the stability of the financial system. The research contributes to the existing body of knowledge in the field of banking and finance by demonstrating the practical application of predictive analytics for credit risk assessment. Overall, this research project serves as a valuable resource for commercial banks, regulators, policymakers, and researchers interested in utilizing predictive analytics for credit risk management. The findings and insights presented in this study pave the way for further research and innovation in the field of financial technology and risk analytics in the banking sector.