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.