Abstract :

Loan Interest Rate Prediction Plays A Pivotal Role In The Financial Sector By Enabling Banks And Lending Institutions To Optimize Their Loan Offerings And Mitigate Risks. Accurate Forecasting Of Loan Interest Rates Ensures That Financial Institutions Can Make Informed Decisions While Maintaining Competitiveness In The Market. This Study Explores The Development Of A Predictive Model For Loan Interest Rates, Leveraging Machine Learning Techniques To Identify Key Factors Influencing Interest Rate Determination. By Analyzing Historical Loan Data, Including Borrower Characteristics Such As Credit Scores, Loan Amounts, And Repayment Durations, As Well As External Economic Indicators, The Model Predicts The Most Appropriate Interest Rate For New Loan Applicants. Several Machine Learning Algorithms, Including Linear Regression, Decision Trees, And Random Forests, Are Compared For Their Efficacy In Modeling Complex Relationships Between The Input Variables And The Interest Rates. The Performance Of The Models Is Evaluated Using Metrics Such As Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), And R-squared To Determine Their Accuracy And Robustness. The Results Demonstrate That Machine Learning-based Models Outperform Traditional Statistical Methods, Providing More Accurate And Dynamic Predictions That Can Adapt To Market Fluctuations. This Research Underscores The Importance Of Data-driven Approaches In Financial Decision-making, Offering A Robust Framework For Integrating Machine Learning Into Loan Interest Rate Forecasting. Future Directions Include Real-time Data Integration, Deep Learning Models, And Further Model Refinement To Improve Prediction Accuracy For Both Lenders And Borrowers.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1217-1223

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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