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.

Thesis Overview

The research project titled "Predictive Analytics for Credit Risk Assessment in Commercial Banking" aims to explore the application of predictive analytics in enhancing the credit risk assessment process within the commercial banking sector. Credit risk assessment is a critical function in banking that involves evaluating the creditworthiness of borrowers to determine the likelihood of default on loans. Traditional credit risk assessment methods rely heavily on historical data and predefined credit scoring models, which may not always capture the dynamic nature of credit risk. Predictive analytics offers a data-driven approach that leverages advanced statistical and machine learning techniques to analyze vast amounts of data and uncover hidden patterns and relationships. By incorporating predictive analytics into credit risk assessment, banks can improve the accuracy and efficiency of their credit decision-making processes. This research project seeks to investigate the potential benefits of predictive analytics in identifying and mitigating credit risk in commercial banking. The project will begin with a comprehensive literature review to explore the existing theories, models, and methodologies related to credit risk assessment and predictive analytics in banking. This literature review will provide a solid foundation for understanding the current state of research in the field and identifying gaps that this study aims to address. The research methodology will involve collecting and analyzing relevant data from commercial banks to develop predictive models for credit risk assessment. Various statistical and machine learning techniques, such as logistic regression, decision trees, and neural networks, will be employed to build predictive models that can accurately predict the likelihood of default for individual borrowers. The findings of this research project are expected to contribute valuable insights into the effectiveness of predictive analytics in credit risk assessment within the commercial banking sector. By identifying key risk factors and developing predictive models that can enhance credit decision-making processes, this study aims to provide practical recommendations for banks to improve their credit risk management practices. Overall, the research project on "Predictive Analytics for Credit Risk Assessment in Commercial Banking" holds significant implications for the banking industry by offering a data-driven approach to credit risk assessment that can enhance risk management practices and ultimately contribute to the financial stability of banks and the economy at large.