Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

Abeer Hamdy,Walid B Hussein,N Mastorakis,V Mladenov,A Bulucea

doi:10.1051/matecconf/20167602039

Abeer Hamdy, Walid B Hussein + Show 3 more

Open Access

https://doi.org/10.1051/matecconf/20167602039

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Abstract

Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution’s profitability. This paper provided a high accuracy credit scoring model that could be utilized with small and large datasets utilizing a principal component analysis (PCA) based breakdown to the significance of the attributes commonly used in the credit scoring models. The proposed credit scoring model applied PCA to acquire the main attributes of the credit scoring data then an ANN classifier to determine the credit worthiness of an individual applicant. The performance of the proposed model was compared to other models in terms of accuracy and training time. Results, based on German dataset showed that the proposed model is superior to others and computationally cheaper. Thus it can be a potential candidate for future credit scoring systems.

Highlights

Credit risk assessment has gained increasing attention in recent years
At epochs 4 and more, the mean square error of the model on the training data is decreased while it increases on the validation data which means that the generated models suffer over fitting problem
It could be observed that the Receiver Operating Characteristic (ROC) curve of the trained classifier is above the diagonal line which implies that the Artificial neural networksArtificial neural network (ANN) classifier can produce a good classification results

Summary

Introduction

Credit risk assessment has gained increasing attention in recent years. Banks and financial institutions have extensively started to consider the credit risk of their customers in order to make decisions to grant credit to new applicants, extend credit or increase credit limit for existing customers and under what terms. Techniques that help with such decisions are called credit and behavioral scoring models [1]. Parametric statistical methods Linear Discriminate Analysis (LDA) and Logistic Regression (LR) have been utilized in developing credit scoring models [26]. Neutral networks is one of the AI techniques that have been applied to credit scoring due to their ability to model both of linear and non-linear functions [8,9,10,11,12,13,14,15,16]. SVMs do not suffer from over fitting problem like ANN and can generalize well The training process consists of running input values over the network with predefined classification output nodes. Testing samples are used to verify the performance of the trained network

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Results

Conclusion