Intelligent System for Credit Risk Management in Financial Institutions

Intelligent System for Credit Risk Management in Financial Institutions

Philip Sarfo-Manu (University of Energy and Natural Resources, Sunyani, Ghana), Gifty Siaw (University of Energy and Natural Resources, Sunyani, Ghana) and Peter Appiahene (University of Energy and Natural Resources, Sunyani, Ghana)
DOI: 10.4018/IJAIML.2019070104
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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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The application of artificial intelligence and machine learning systems for credit risk management has received extensive academic research in recent years. Credit risk means the probability of non-repayment of bank financial facilities granted to investors (Nazari, 2013; Weber et al., 2015). Credit risk management is defined as identification, measurement, monitoring and control of risk arising from the possibility of default in loan repayments (Kithinji, 2010; Wu et al., 2014). Poor credit risk management or evaluation constitutes the major reason financial institutions become bankrupt due to the huge amount of money being locked up when borrowers defaults. This is the reason why the development of a great variety of strategies to implement reliable prediction models has attracted considerable attention both from academicians and financial analysts over the last decades. Several researchers have explored and analyze the ability machine learning to develop models and software to forecast the credit risk. Alshatti (2015) investigated the impact of credit risk management on bank’s financial performance, through ascertaining the credit risk management and financial performance indicators, and to find a pragmatic proof of the degree to which credit risk management affects banks’ financial performance. Credit scoring has been acknowledged as a two-fold classification technique, distinguishing applicants into two groups: good credit and bad credit, based on features such as gender, age, occupation, and salary. These conclude the applicability of loans for applicants. There are two conventional classification techniques, statistical techniques and machine learning techniques (He et al., 2018).

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