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Author(s):

Dr. U. Nilabar Nisha | Murugan K | Dhanush R | Ajith Kumar E | Nithin Kumar V
Keywords:

Loan Approval, Machine Learning, Data Analytics, Random Forest, Prediction, Credit Score, Classification, Financial System, Automation, Interpretability, Risk Assessment, Decision Support
Abstract:
The Process Of Loan Approval Plays A Vital Role In Financial Institutions, Requiring Accurate And Timely Decision-making. Traditional Systems Rely Heavily On Manual Evaluation, Which Often Leads To Inconsistency And Delays. This Study Presents An Interpretable Financial Decision Support System For Loan Approval Using Data Analytics Techniques. The System Leverages Historical Applicant Data To Predict Loan Eligibility With Improved Precision. Key Attributes Such As Income, Credit Score, Loan Amount, And Asset Values Are Considered During Analysis. Machine Learning Models Are Applied To Identify Patterns And Relationships Within The Data. Among Various Algorithms, Random Forest Is Emphasized For Its Interpretability And Performance. The Proposed System Minimizes Human Bias And Enhances Transparency In Decisions. A User-friendly Interface Enables Real-time Prediction Of Loan Approval Status. The Overall Approach Improves Efficiency, Consistency, And Reliability In Financial Decision-making. The Model Is Designed To Be Scalable And Adaptable To Different Financial Datasets. It Also Supports Data-driven Policy Formulation In Modern Banking Systems.: