CONSTRUCTING A PREDICTIVE MODEL FOR CREDIT RISK ASSESSMENT IN THE BANKING SECTOR USING MACHINE LEARNING TECHNIQUES

Abstract

A commercial bank is a type of financial intermediary that takes deposits and uses them to fund lending operations. Credit risk occurs when a debtor fails to make a loan or other credit-related payments. Banks often regulate and manage risk through the discipline known as risk management. Creating and implementing a technology that can effectively support risk management is crucial. This study used Awash Bank Share Company as a case study to examine how machine-learning techniques support loan risk assessment. Following the acquisition of credit data from Awash Bank, appropriate features were selected and data preparation procedures, including data preprocessing, were accomplished. Recursive Feature Elimination (RFE) was utilized for attribute selection during data preprocessing, and efforts were made to clean the data by handling missing values and correcting errors. The important attributes that were identified are Loan Type, Amount Granted, Mode of Repayment, Principal, Interest Rate, Days Overdue, Net worth, Current ratio, Quick Ratio, Debt to Asset ratio, Debt to Equity Ratio, Net profit margin, Liabilities, Total Capital, Total Asset, and target class Status. To address data imbalance and control model overfitting, SMOTE and L2 regularization were applied. This study employs two data splitting techniques (Percentage split and 10-fold cross-validation). For constructing a prediction model machine learning algorithms, such as MLP (Multi-Layer Perceptron), SVM (Support Vector Machine), and LR (Logistic Regression) are employed to identify and classify credit Status into Pass or Loss categories. According to the experimental results, the MLP classifier achieves a higher classification accuracy of 99.35% because MLP's ability to learn complicated patterns as well as its independence from data sequence dependencies led to its excellent performance in this context and hence we suggest MLP for constructing an applicable system for credit risk assessment. So, this study successfully constructs a predictive model with higher accuracy for Credit Risk Assessment in the Banking Sector to identify customers' credit repayment ability by using Machine Learning Techniques. However, the study constructs a predictive model based on the data obtained from one bank, Awash Bank. It is therefore our recommendation to collect data from different banks so as to construct a generic model.