An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

Abstract

The Korean e-commerce market represents a large percentage of the global retail distribution market, a market that continues to grow each year, and online payments are rapidly becoming a mainstream payment method. As e-commerce becomes more active, many companies that support electronic payments are increasing the number of franchisees. Electronic payments have become an indispensable part of people’s lives. However, the types of statistical information on the results of electronic payment transactions are not consistent across companies, and it is difficult to automatically determine the error status of a transaction if no one directly confirms the error messages generated during payment. To address these issues, we propose an optimized LSTM model. In this study, we classify the error content in statistical information based on natural language processing to determine the error status of the current failed transaction. We collected 11,865 response messages from various vendors and financial companies and labelled them with an LSTM classifier model to create a dataset. We then trained this dataset with simple RNN, LSTM, and GRU models and compared their performance. The results show that the optimized LSTM model with the attention layer added to the dropout layer and the bidirectional recursive layer achieves an accuracy of about 92% or more. When the model is applied to e-commerce services, any error in the transaction status of the system can be automatically detected by the model.