Credit Worthiness Prediction Model Using Artificial Neural Network

The study focused on developing a creditworthiness prediction model utilizing artificial neural network. Credit risk evaluation has a relevant role for financial institutions, as lending could result in real and immediate losses. In particular, default prediction was one of the most challenging activities in managing credit risk. The objective was to enhance the accuracy and reliability of credit risk assessments by leveraging the computational power and learning capabilities of artificial neural networks. The parameters of the dataset include the customer's place of work, loan history, monthly salary, loan amount, transaction history, and credit history, all stored in the trained database. When a customer comes to apply for a loan, the system checks if the user is qualified based on these parameters and then approve or disapprove the loan accordingly. Rigorous testing and validation were conducted to ensure the model's robustness and generalizability. The results demonstrated that the neural network-based model significantly outperformed traditional statistical methods, providing more precise predictions of creditworthiness. An Object-Oriented Analysis and Design Methodology (OOADM) approach, which incorporated Unified Modeling Language for analysis and design, was used. The development stage was completed using a set of software tools, including Python and the MySQL database system.

Credit worthiness, Prediction, Credit risk assessment, Artificial neural network, Loan

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