Abstract
With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.
Highlights
The case with many financial institutions such as banks is that credit lending products such as credit cards, personal loans and mortgages are the center of their dealings, and proper lending will yield huge gains
It is not possible that all customers will act the same way when it comes to financial performance, there should be distinguishable treatment between customers who qualify for certain profitable requirements, based on their repayment and purchasing behaviour customers exhibiting such behaviour can be offered greater incentives and rewards [1]
To outline the discrimination power of the Bidirectional Long-Short Term Memory (LSTM) model performance measures are calculated for all active customers, but for different subsets of them: (1) Customers with one missed payment during the last 2 months are the group that generally have a low risk of default, but the recent missed payment is a reason to look at those in this group more closely
Summary
The case with many financial institutions such as banks is that credit lending products such as credit cards, personal loans and mortgages are the center of their dealings, and proper lending will yield huge gains. As a result, it is important for financial institutions and banks to get new customers and ensure to keep profitable ones. Article [2] defined credit scoring as the means of analyzing the likelihood of applicant to falter in their repayments, or not. In Anderson [3] authors defined it by dividing the term into two parts: the first is ‘credit’, which means to buy an item and pay afterwards, and the second is ‘scoring’, which is alike with the method used for credit cards
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