In today’s data-driven world, the financial sector is a bastion of information and the repository of countless transactions, interactions and decisions that together shape the financial destiny of the economy and individuals. In the complex business environment of this industry, the origination and management of loans are cornerstones. The approval of loans is influenced by a range of factors that impact both the economic environment and, more significantly, the lives of individuals.. The allocation of credit resources reflects in many ways broader dynamics of wealth distribution, economic growth, and financial stability. This thesis examines the application of statistical algorithms and machine learning techniques in financial loan evaluation. The study focuses on historical credit data and aims to build predictive models for credit approvals and denials. The study aims to streamline lending processes, fortify risk management, and enhance decision-making in banking institutions. By leveraging diverse predictive modeling approaches, the research seeks to contribute to more efficient, transparent, and equitable lending practices, emphasizing the ethical implications of data analysis in the financial sector.