SECURED LOAN PREDICTION SYSTEM USING ARTIFICIAL NEURAL NETWORK

Loan approval is an essential factor that decides the loss or gains a financial institution would accrue at the end of a fiscal year. Banks are looking for ways to ensure that these loans are paid back within the specified period. Therefore, this study aims to develop a loan prediction system using Artificial Neural Network that will determine whether a loan is a good or bad one and whether a loan is a payable debt or bad debt. The system can also assist to predict whether a loan applicant would default in repayment or not. The study used an Artificial Neural Network algorithm to develop a loan prediction scheme. The system was designed and implemented using Python as the programming language, Hypertext Mark-Up Language (HTML), Cascading Style sheet (CSS) for the front end, and then PHP as the backend. The system also used the confusion matrix as the performance metrics to evaluate the system accuracy. The result shows that the system has 92% accuracy which showed that the developed system predicted well and can predict whether a loan applicant would default in repayment or not. The system can also predict whether a loan is a bad debtor payment one. The system was finally compared with other previous researches using the accuracy of the system and it was concluded that the proposed system performed better than the previous researches.