Time-Weighted Cumulative LSTM Method Using Log Data for Predicting Credit Card Customer Turnover

One of the most important challenges for credit card companies is to retain customers. To achieve this, they predict whether customers will use credit cards and respond preemptively through marketing. Improper prediction leads to an increase in marketing costs; therefore, improving the performance of the prediction model is an important challenge for credit card companies. Therefore, credit card companies are working toward increasing the predictive power of the model, but the data used for learning are limited to formal data, such as card payment data; the log history stored on the application is not used. Prediction models using sequence data, such as log history, are being studied. However, no studies so far have appropriately utilized card payment data, log history, and information on the timing of the elements constituting the sequence. In this paper, we propose a method for predicting credit card turnover using application log and credit card payment data. The method embeds log data and weights each log using a term-weighting function. In addition, the time difference between the day customers visit the app and the last day of the month in which they leave is calculated. The weight value is higher when the timespan is shorter. The weighted log data were trained with a 2-layer LSTM model and its output layer was combined with the output layer trained with the card payment data for customer turnover prediction. It was verified that the model presented in this paper had a higher performance than the model using only card payment data and the model using only a time-weighted log sequence.