OPTIMALISASI PENILAIAN KELAYAKAN KREDIT DENGAN ALGORITMA BACKPROPAGATION

Abstract

The implementation of the backpropagation algorithm in customer loan eligibility using Artificial Neural Networks (ANN) with the backpropagation method represents an effective approach for evaluating and predicting loan eligibility. This algorithm allows the use of historical data to train a model capable of identifying complex patterns in customer data, including payment behavior, credit history, and other relevant factors. By leveraging this technique, financial institutions can enhance accuracy in assessing credit risk, optimize loan decisions, and reduce the risk of non-performing loans. Loan eligibility assessment is a crucial aspect in the banking industry for minimizing credit risk and increasing profitability. This study proposes a loan eligibility assessment method using the Backpropagation algorithm within Artificial Neural Networks (ANN). The method aims to identify customer eligibility based on historical data and related features such as income, credit history, and age. The research findings indicate that the ANN Backpropagation model can provide accurate predictions of customer loan eligibility with lower error rates compared to traditional methods. These findings suggest that applying the ANN Backpropagation algorithm can enhance the effectiveness of the credit assessment process, minimize credit default risk, and potentially optimize loan decisions in the banking industry.