Project Abstract

Abstract
Credit scoring is a critical process in retail banking that involves assessing the creditworthiness of individuals applying for loans or credit. Traditional credit scoring methods rely on predefined rules and statistical models, which may not capture the complex patterns present in the data. This research explores the application of machine learning algorithms to enhance credit scoring accuracy and efficiency in retail banking. The primary objective of this study is to evaluate the performance of various machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, in predicting creditworthiness. A comprehensive literature review is conducted to provide insights into the existing methodologies and challenges in credit scoring and machine learning applications in banking. The research methodology involves collecting a dataset of historical credit application data from a retail bank and pre-processing the data to ensure quality and consistency. The dataset is then divided into training and testing sets for model development and evaluation. Various machine learning algorithms are implemented and compared based on their predictive performance metrics such as accuracy, precision, recall, and F1-score. The findings of this study reveal that machine learning algorithms, particularly ensemble methods like random forests, outperform traditional credit scoring models in terms of predictive accuracy and robustness. These algorithms demonstrate the ability to capture complex patterns and relationships within the data, leading to more reliable credit scoring decisions. The discussion of findings highlights the implications of adopting machine learning algorithms for credit scoring in retail banking, including improved risk assessment, reduced default rates, and enhanced customer experience. The study also identifies potential challenges and limitations in implementing machine learning models in a banking environment, such as data privacy concerns and interpretability issues. In conclusion, the research underscores the significance of leveraging machine learning algorithms for credit scoring in retail banking to enhance decision-making processes and mitigate financial risks. The study contributes to the existing body of knowledge by demonstrating the practical benefits of advanced analytics in improving credit assessment outcomes. Future research directions include exploring the integration of alternative data sources and advanced model interpretability techniques to further enhance the credit scoring process in retail banking.

Project Overview