A deep learning model for behavioural credit scoring in banks

ORCID Identifiers

0000-0001-9315-0670

Document Type

Article

Source of Publication

Neural Computing and Applications

Publication Date

1-14-2022

Abstract

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 concerning three aspects: the probability of single and consecutive missed payments for credit card customers, the purchasing behaviour of customers, and grouping customers based on a mathematical expectation of loss. Two models are developed: the first provides the probability of a missed payment during the next month for each customer, which is described as Missed payment prediction Long Short Term Memory model (MP-LSTM), whilst the second estimates the total monthly amount of purchases, which is defined as Purchase Estimation Prediction Long Short Term Memory model (PE-LSTM). Based on both models, a customer behavioural grouping is provided, which can be helpful for the bank’s decision-making. Both models are trained on real credit card transactional datasets. Customer behavioural scores are analysed using classical performance evaluation measures. Calibration analysis of MP-LSTM scores showed that they could be considered as probabilities of missed payments. Obtained purchase estimations were analysed using mean square error and absolute error. The MP-LSTM model was compared to four traditional well-known machine learning algorithms. Experimental results show that, compared with conventional methods based on feature extraction, the consumer credit scoring method based on the MP-LSTM neural network has significantly improved consumer credit scoring.

ISSN

1433-3058

Publisher

Springer Nature

Disciplines

Business | Computer Sciences

Keywords

LSTM, Neural networks, Behavioural scoring, Machine learning, Classification

Scopus ID

85123077001

Indexed in Scopus

yes

Open Access

no

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