Artificial Intelligence and Credit Risk Management of Deposit Money Banks in Rivers State, Nigeria

Abstract

This study examined the relationship between Artificial Intelligence (AI) and credit risk
management of deposit money banks in Rivers State, Nigeria. The methodology employed is
survey design involving data collection using a self-administered questionnaire. The data was
collected from deposit money banks in Rivers State. Out of the 14 Listed Deposit money Banks in
Nigeria, 13 banks were selected based on a simple random Sampling Technique. The study
adopted statistical Package for Statistical sciences (SPSS) version 20. Regression analysis was
used to predict the effect of the independent variable on the dependent variable and was also used
to either accept or reject the null hypothesis. Validity and reliability test was carried out. The study
modeled credit risk management as the function of an automated chatbot banking, a deep learning
machine, a machine learning solution, and Natural language processing. The researcher concludes
that artificial intelligence significantly affects the credit risk management of deposit money banks
in Nigeria. The study recommends that banks should continue to integrate AI technologies across
various departments, including credit assessment, fraud detection, and operational risk
management. Banks should prioritize the adoption of AI systems that can automate routine tasks,
improve decision-making processes, and reduce human error; ultimately enhancing risk
management capabilities. They should also collaborate with fintech firms to enhance their credit
scoring models, ensuring they are leveraging the most up-to-date data and technologies.