Modelling customers credit card behaviour using bidirectional LSTM neural networks

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.

Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending

“…For instance, Pławiak et al (2019) and Pławiak et al (2020) established deep genetic cascade ensemble models and provided state-ofthe-art performance in Statlog German and Australian datasets. Long short-term memory (LSTM) networks have also been applied and achieve sound predictive accuracy (Ala'raj et al, 2021;Wang et al, 2018). Shen et al (2021) further proposed a novel deep learning ensemble credit scoring model by incorporating the LSTM network and AdaBoost in an ensemble framework, which achieved comparative results in the experimental test.…”

Consumers' perceptions of intention to use a credit card: perceived risk and security

“…However, the results were mixed: public relations had a substantial negative impact (Cornea, 2021), a significant positive impact (Hussein et al, 2021), or no impact on consumer willingness to use credit cards (Hussein et al, 2021;Boden et al, 2020). Credit cards are a type of technology that may be utilized on electronic devices to fulfill two basic functions: payment and credit (Ala'raj et al, 2021). Credit cardholders have the option of purchasing first and paying later, thanks to the bank's guarantee (Lebichot et al, 2021).…”

“…Credit cardholders have the option of purchasing first and paying later, thanks to the bank's guarantee (Lebichot et al, 2021). As a result, the cardholder's issuing bank will pay the biller on their behalf, and the cardholder is responsible for timely and complete payback of all payments (Ala'raj et al, 2021). Credit cards are getting increasingly popular and widely utilized in modern transactions throughout the world (Jamshidi & Kuanova, 2020).…”

Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability

“…The data mining technique using the customer classification approach is an approach that is widely used to find patterns, analyze and predict [5]. With a customer classification approach based on existing datasets, we can predict a customer's payment capability [6]. Meanwhile, manually, it is very difficult to accurately predict the capability of customer credit payments based on the dataset they have.…”