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.

随着消费者信用的快速增长和海量金融数据的发展，有效的信用评分模型变得至关重要。研究人员使用统计和人工智能（AI）技术开发了复杂的信用评分模型，以帮助银行和金融机构支持其财务决策。神经网络被认为是金融和商业应用中使用最广泛的技术。因此，本文的主要目的是通过对以下两个方面的建模和预测消费者行为来帮助银行管理人员使用机器学习对信用卡客户进行评分：信用卡客户一次或连续错付的概率。提议的模型基于双向长期短期记忆（LSTM）模型，以给出每个客户下个月未付款的可能性。该模型在真实的信用卡数据集上进行了训练，并使用经典的度量标准（例如准确度，曲线下面积，Brier得分，Kolmogorov-Smirnov检验和H度量）对客户行为得分进行了分析。LSTM模型分数的校准分析表明，可以将其视为错过付款的概率。将LSTM模型与四种传统的机器学习算法进行了比较：支持向量机，随机森林，多层感知器神经网络和逻辑回归。实验结果表明，与传统方法相比，基于LSTM神经网络的消费者信用评分方法显着提高了消费者信用评分。