IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 5 2024
DOI: 10.56201/ijcsmt.v10.no5.2024.pg123.134

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Prediction of Credit Default Risk in Financial Institutions Using Artificial Neural Network

Anyanwu Onyekachi Julian and Amanze Bethran Chibuike

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Abstract

This research study, examined the Prediction of Credit Default Risk in Financial Institutions using Artificial Neural Network. Credit default risk has remained one of the important fundamental and critical issues widely studied in the financial institutions in Nigeria. Credit Default risk comes into play when a loan borrower fails to repay his loan within the agreed financial contract. Banks and other financial institutions depend heavily on statistical and machine learning method in predicting loan default to the potential losses of granted loans. These machine learning applications cannot achieve full potential prediction without the semantic context in the data. Neural Network initiate the behavior of the human brain to solve both linear and non-linear statistical problems. The study observe that credit risk is the greatest and leading risk in the banking sector as its effects have crippled several financial institutions and have led to the failure of many. Therefore, the study proposed the adoption of Neural Network in predicting credit default risk to improve the prediction model’s accuracy and interoperability. Agile methodology, structured system analysis design methodology (SSADM) was used in the software development to get the total records of credit defaulters. This study designed a system for prediction of Credit Default Risk using Artificial Neural Network. The system is an efficient, accurate and reliable predictive tool which can be employed by financial institutions and lender organizations to solve and manage problem of credit default risk. The programming language PHP Script will be use for the software and My Structured Query Language (MySQL) for database.

keywords:

Artificial Neural Network, Credit Default Risk, Prediction model, Statistical

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