Thesis Abstract

Abstract
The financial stability of commercial banking institutions relies heavily on the effective assessment and management of credit risk. In recent years, advancements in predictive modeling techniques have provided new avenues for enhancing the accuracy and efficiency of credit risk assessment processes. This thesis explores the application of predictive modeling in the context of credit risk assessment within commercial banking, with a specific focus on developing a model that can effectively predict and quantify credit risk for individual borrowers. The study begins with an in-depth examination of the current landscape of credit risk assessment in commercial banking, highlighting the challenges and limitations faced by traditional methods. Through a comprehensive review of existing literature, the research establishes a solid theoretical foundation for the development and implementation of predictive modeling techniques in credit risk assessment. Building on the literature review, the research methodology section outlines the approach taken to construct and validate the predictive model. Utilizing a dataset of historical credit data, the study employs machine learning algorithms and statistical techniques to train the model and evaluate its predictive performance. The methodology also addresses key considerations such as data preprocessing, feature selection, and model evaluation metrics. The findings of the study are presented and discussed in detail in Chapter Four, where the performance of the predictive model is assessed based on various criteria such as accuracy, sensitivity, and specificity. The results demonstrate the effectiveness of the model in predicting credit risk for individual borrowers, providing valuable insights for commercial banking institutions to make informed lending decisions. In conclusion, this thesis contributes to the existing body of knowledge on credit risk assessment by demonstrating the potential of predictive modeling techniques in enhancing the accuracy and efficiency of risk evaluation processes in commercial banking. The study highlights the significance of incorporating predictive modeling into credit risk management practices and underscores the importance of leveraging data-driven approaches to mitigate credit risk and safeguard the financial health of banking institutions.

Thesis Overview