Enhancing Loan Decision-Making with Machine Learning in Banking
K.Papayamma1, M.Yeswanth2, N.Vijayalakshmi3 , N.Akhil4, S.Chakri Arvind Teja5
1Assistant Professor, Dept of CSE AI-ML, Raghu Engineering of college
2Student, Dept of CSE AI-ML, Raghu Institute of Technology
3Student, Dept of CSE AI-ML, Raghu Institute of Technology
4student, Dept of CSE AI-ML, Raghu Institute of Technology
5student, Dept of CSE AI-ML, Raghu Institute of Technology
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Abstract - Loan approval is an important process in the banking sector. This process reduces risks and helps ensure profitability. The traditional method of loan approval does this, either by manual assessment or by a rule-based system. There comes a time when acceptance or rejection of the loan application has to be done. However, it takes much time and not always leads to optimal decisions. These methods are even inappropriate while dealing with complex datasets and different attributes of customers. Henceforth, to overcome the above drawbacks, we propose a system which comprises an ML-based system and ensures automation of the decision-making process of a bank loan in terms of high accuracy. In many financial institutions, loan approval processes are largely dependent on manual evaluations and simple rules-based systems. Loan officers analyze the creditworthiness of applicants based on a set of fixed parameters, such as income, employment status, credit score, and existing debt. While effective to an extent, these methods may overlook non-linear relationships between variables, leading to higher rates of loan default or missed opportunities to approve creditworthy applicants. Additionally, manual processing is resource-intensive and prone to human biases or errors. The proposed system leverages machine learning algorithms to enhance the efficiency and accuracy of loan approval decisions. By training on historical loan data, the system can automatically predict the likelihood of loan approval or rejection based on a variety of features.
Key Words : Automated Process, Risk Assessment, Predective Analysis, Bias Mitigation, Real-Time Decision Making, Scalability, Feature Importance And Interpretability.
