Thesis Abstract

Abstract
The banking sector plays a critical role in the economy by facilitating financial transactions and providing credit to individuals and businesses. Effective credit risk assessment is essential for banks to make informed lending decisions and mitigate potential losses. Traditional methods of credit risk assessment have limitations in accurately predicting default risks, leading to potential financial instability. In response to these challenges, this study focuses on the application of predictive analytics in credit risk assessment within the banking sector. This research project aims to explore the effectiveness of predictive analytics models in assessing credit risk and enhancing the overall risk management practices in banking institutions. The study will leverage historical data on loan performance, customer profiles, economic indicators, and other relevant variables to develop predictive models that can forecast the likelihood of default or delinquency for individual borrowers. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review on credit risk assessment, predictive analytics, machine learning algorithms, and previous studies related to the application of predictive analytics in the banking sector. Chapter 3 outlines the research methodology, detailing the research design, data collection methods, variables selection, model development, and validation techniques. The chapter also discusses ethical considerations and potential biases that may impact the research findings. Chapter 4 presents an in-depth analysis of the research findings, including the performance evaluation of the predictive models, comparison with traditional methods, and insights derived from the data analysis. The results of the study will provide valuable insights into the effectiveness of predictive analytics in credit risk assessment and its implications for enhancing risk management practices in the banking sector. The findings will contribute to the existing literature on credit risk assessment and provide practical recommendations for banks to improve their lending decisions and reduce default risks. In conclusion, this research project underscores the importance of leveraging predictive analytics to enhance credit risk assessment practices in the banking sector. By developing accurate and reliable predictive models, banks can improve their risk management processes, optimize lending decisions, and ultimately contribute to financial stability and sustainable economic growth.

Thesis Overview