Project Abstract

Abstract
The use of machine learning algorithms in credit scoring has gained significant attention in the banking and finance industry due to its potential to improve accuracy and efficiency in assessing creditworthiness. This research project aims to explore the application of machine learning techniques in credit scoring for banks. The study will investigate how various machine learning models can be utilized to predict credit risk and enhance the decision-making process in lending. The research will begin with a comprehensive literature review to examine existing studies on credit scoring, machine learning, and their intersection in the banking sector. This review will provide insights into the current trends, challenges, and opportunities in applying machine learning algorithms for credit assessment. The methodology chapter will outline the research design, data collection methods, and the selection of machine learning models to be used in the study. Various algorithms such as logistic regression, decision trees, random forests, and neural networks will be applied to analyze historical credit data and predict creditworthiness. The findings chapter will present the results of the analysis, including the performance comparison of different machine learning models in credit scoring. The discussion will delve into the implications of the findings for banks, highlighting the potential benefits and challenges of implementing machine learning in credit risk assessment. In conclusion, this research project will provide valuable insights into the application of machine learning in credit scoring for banks. The study aims to contribute to the existing body of knowledge on credit risk assessment and offer practical recommendations for financial institutions seeking to enhance their credit evaluation processes through advanced analytics.

Project Overview