Application of Machine Learning in Credit Scoring for Improved Risk Management in Banking

Project Abstract

Credit scoring is a crucial aspect of risk management in the banking industry, as it helps financial institutions assess the creditworthiness of potential borrowers. Traditional credit scoring models have limitations in accurately predicting credit risk, leading to potential losses for banks and financial institutions. In recent years, machine learning techniques have gained popularity for their ability to analyze large datasets and extract valuable insights to enhance credit scoring models. This research project aims to investigate the application of machine learning in credit scoring to improve risk management practices in the banking sector. The study will focus on developing and evaluating machine learning models that can effectively predict credit risk by analyzing various financial and non-financial data points. By leveraging advanced algorithms such as random forests, support vector machines, and neural networks, the research aims to enhance the accuracy and efficiency of credit scoring processes. The research will be conducted using a dataset obtained from a leading financial institution, containing information on historical loan applications, borrower demographics, credit histories, and repayment behaviors. The dataset will be preprocessed to handle missing values, outliers, and feature engineering to extract relevant information for model training. Various machine learning algorithms will be implemented and compared to identify the most effective model for credit scoring. The methodology chapter will outline the research design, data collection methods, model development, and evaluation strategies. The study will employ a quantitative research approach to analyze the performance of machine learning models in credit scoring. Evaluation metrics such as accuracy, precision, recall, and F1 score will be used to assess the predictive capabilities of the models. The findings chapter will present a detailed discussion of the results obtained from the model evaluation process. The research will highlight the strengths and limitations of different machine learning algorithms in credit scoring and provide insights into the factors that influence credit risk assessment. Additionally, the chapter will discuss the implications of the findings for banks and financial institutions looking to adopt machine learning in their risk management practices. In conclusion, this research project will contribute to the existing literature on credit scoring by demonstrating the potential of machine learning techniques in improving risk management in the banking sector. By developing more accurate and efficient credit scoring models, financial institutions can make informed lending decisions, reduce default rates, and enhance their overall risk management processes. The study will provide valuable insights for practitioners, policymakers, and researchers interested in the application of machine learning in credit risk assessment.

Project Overview