Abstract:
Financial organizations such as banks have experienced an increase in demand for loans from
borrowers over the years. These organizations are highly interested in knowing whether a
borrower can pay back if granted the loan requested. Granting loans to defaulters can cripple
the business, hence, these financial organizations are compelled to evaluate credit worthiness
of clients using the vast volume of historical data related to financial position of individuals
and organizations. Credit scoring is a technique used in predicting the probability that a loan
applicant, existing borrower, or counterparty will default. Automated Credit scoring
mechanism has replaced onerous, error-prone labour-intensive manual reviews that were less
transparent and lacks statistical-soundness in almost all financial organizations. This research
focuses on the development of a credit scoring model using Rough Set Theory (RST) and
Multi-Layer Perceptron (MLP) Neural Network. RST was used for feature selection while
ANN trained with backpropagation was used for classification. This research used two credit
scoring datasets; Australian and German credit dataset. Data pre-processing and machine
learning were performed using the Anaconda software. This research compares the result
obtained from the RST and MLP with Decision Tree, Logistic Regression, Random Forest,
Support Vector Machine, Nayes Bayes, K-Nearest Neighbour and ANN using standard
evaluation metric to ascertain its performance on the two datasets. RST and MLP outperformed
all the models with an accuracy of 90% for Australian dataset and 87% for German dataset.
We also observed an improvement in accuracy in some of the models used when feature
selection using RST was performed prior to classification. This research contributed a credit
scoring model with improved performance while saving the computational costs.
