Developing a predictive model for credit risk assessment in commercial banking using machine learning algorithms

Thesis Abstract

Abstract
The banking industry plays a crucial role in the global economy by facilitating financial transactions and providing credit to individuals and businesses. Credit risk assessment is a critical process in commercial banking that involves evaluating the creditworthiness of borrowers to minimize the risk of default. Traditional credit risk assessment methods rely on historical data and statistical models, which may not always capture the complex and dynamic nature of credit risk. In recent years, machine learning algorithms have emerged as powerful tools for predictive modeling in various industries, including banking and finance. This thesis focuses on developing a predictive model for credit risk assessment in commercial banking using machine learning algorithms. The research aims to enhance the accuracy and efficiency of credit risk assessment processes by leveraging the capabilities of machine learning techniques. The study will explore the application of various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, to predict credit risk in commercial banking. The research methodology will involve collecting a large dataset of historical credit information from a commercial bank and preprocessing the data to ensure its quality and relevance. Feature selection techniques will be employed to identify the most important variables that influence credit risk. The selected machine learning algorithms will be trained and evaluated using the dataset to build predictive models for credit risk assessment. The findings of this study are expected to demonstrate the effectiveness of machine learning algorithms in improving the accuracy and efficiency of credit risk assessment in commercial banking. By developing a predictive model that can accurately predict credit risk, banks can make more informed lending decisions, reduce the incidence of defaults, and ultimately improve their overall risk management practices. The significance of this research lies in its potential to contribute to the advancement of credit risk assessment practices in commercial banking through the integration of machine learning algorithms. The findings of this study can provide valuable insights for banking institutions looking to enhance their risk management processes and improve the quality of their lending decisions. In conclusion, this thesis presents a comprehensive investigation into the development of a predictive model for credit risk assessment in commercial banking using machine learning algorithms. The research aims to bridge the gap between traditional credit risk assessment methods and cutting-edge machine learning techniques to enable more accurate and efficient credit risk prediction. The outcomes of this study have the potential to revolutionize credit risk assessment practices in commercial banking and pave the way for more sophisticated and data-driven risk management strategies.