Enhancing loan default prediction with smart loan recommendation

This paper presents the development of a LoanDefault Prediction System integrated with a Smart Loan Recommendation System, addressing the persistent challenges faced by financial institutions in managing loan defaults.Leveraging Artificial Neural Networks (ANN) for predictive modeling, our approach enhances decision-making in loan approvals by accurately estimating the probability of borrower default.Traditional statistical models often fall short in capturing complex relationships between borrower characteristics and loan performance; therefore, machine learning, particularly ANNs, offers a more robust solution.Furthermore, the paper introduces a Smart Loan Recommendation System designed to suggest optimized loan terms-such as revised amounts, tenures, and EMI structures-for borrowers identified as high-risk.Inspired by recent advances in personalized financial systems, this dual-module approach not only minimizes potential defaults but also empowers borrowers with manageable and personalized financial options.Experimental results demonstrate that the integrated system significantly reduces default rates and enhances both financial stability and user satisfaction.