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Intelligent System for Credit Risk Management in Financial Institutions

Intelligent System for Credit Risk Management in Financial Institutions

Philip Sarfo-Manu, Gifty Siaw, Peter Appiahene
Copyright: © 2019 |Volume: 9 |Issue: 2 |Pages: 11
EISBN13: 9781522566519|ISSN: 2642-1577|EISSN: 2642-1585|DOI: 10.4018/IJAIML.2019070104
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MLA

Sarfo-Manu, Philip, et al. "Intelligent System for Credit Risk Management in Financial Institutions." IJAIML vol.9, no.2 2019: pp.57-67. http://doi.org/10.4018/IJAIML.2019070104

APA

Sarfo-Manu, P., Siaw, G., & Appiahene, P. (2019). Intelligent System for Credit Risk Management in Financial Institutions. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 9(2), 57-67. http://doi.org/10.4018/IJAIML.2019070104

Chicago

Sarfo-Manu, Philip, Gifty Siaw, and Peter Appiahene. "Intelligent System for Credit Risk Management in Financial Institutions," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 9, no.2: 57-67. http://doi.org/10.4018/IJAIML.2019070104

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Abstract

Credit crunch is an alarming challenge facing financial institutions in Ghana due to their inability to manage credit risk. Failure to manage credit risk may lead to customers defaulting and institutions becoming bankrupt, making it a major concern for financial institutions and the government. The assessment and evaluation of loan applications based on a loan officer's subjective assessment and human judgment is inefficient, inconsistent, non-uniform, and time consuming. Therefore, a knowledge discovery tool is required to help in decision making regarding the approval of loan application. The aim of this project is to develop an intelligent system based on a decision tree model to manage credit risk. Data was obtained from the bank loan histories. The data is comprised of four hundred observations with seven variables: client age, amount requested, dependents, collateral value, employment sector, employment type, and results. The results of study suggest that the proposed system can be used to predict client eligibility for loans with an accuracy rate of 70%.

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