Thesis Abstract

Abstract
This thesis focuses on the development and implementation of predictive modeling techniques for credit risk assessment in the banking sector. Credit risk assessment is a crucial process for banks to evaluate the creditworthiness of borrowers and make informed lending decisions. Traditional credit risk assessment methods often rely on historical data and statistical analysis. However, with the increasing complexity of financial markets and the availability of large volumes of data, there is a growing need for more advanced and accurate predictive models to assess credit risk. The primary objective of this study is to design and evaluate predictive modeling techniques that can enhance the accuracy and efficiency of credit risk assessment in banking. The research methodology includes a comprehensive literature review to identify existing models and techniques used in credit risk assessment. The study also involves the collection of relevant data from banking institutions to develop and test the proposed predictive models. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a detailed literature review on credit risk assessment, predictive modeling techniques, and their applications in the banking sector. The literature review covers key concepts such as credit scoring models, machine learning algorithms, and risk management practices. Chapter Three outlines the research methodology, including data collection methods, model development techniques, model evaluation criteria, and validation procedures. The chapter also discusses the selection of variables, data preprocessing steps, and model tuning processes to optimize the predictive performance of the credit risk assessment models. Chapter Four presents the findings of the study, including the performance evaluation of the developed predictive models in predicting credit risk. The chapter discusses the accuracy, sensitivity, specificity, and other key metrics used to assess the effectiveness of the models in differentiating between good and bad credit risks. Chapter Five concludes the thesis with a summary of the research findings, implications for banking institutions, limitations of the study, and recommendations for future research. The study contributes to the existing literature by proposing advanced predictive modeling techniques that can enhance credit risk assessment practices in the banking sector. In conclusion, this thesis provides valuable insights into the application of predictive modeling for credit risk assessment in banking and offers practical recommendations for banks to improve their risk management processes. The proposed models have the potential to enhance decision-making accuracy, reduce credit losses, and strengthen the overall financial stability of banking institutions.

Thesis Overview