RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions

Credit card (CC) fraud has been a persistent problem and has affected financial organizations. Traditional machine learning (ML) algorithms are ineffective owing to the increased attack space, and techniques such as long short-term memory (LSTM) have shown promising results in detecting CC fraud patterns. However, owing to the black box nature of the LSTM model, the decision-making process could be improved. Thus, in this paper, we propose a scheme, RaKShA, which presents explainable artificial intelligence (XAI) to help understand and interpret the behavior of black box models. XAI is formally used to interpret these black box models; however, we used XAI to extract essential features from the CC fraud dataset, consequently improving the performance of the LSTM model. The XAI was integrated with LSTM to form an explainable LSTM (X-LSTM) model. The proposed approach takes preprocessed data and feeds it to the XAI model, which computes the variable importance plot for the dataset, which simplifies the feature selection. Then, the data are presented to the LSTM model, and the output classification is stored in a smart contract (SC), ensuring no tampering with the results. The final data are stored on the blockchain (BC), which forms trusted and chronological ledger entries. We have considered two open-source CC datasets. We obtain an accuracy of 99.8% with our proposed X-LSTM model over 50 epochs compared to 85% without XAI (simple LSTM model). We present the gas fee requirements, IPFS bandwidth, and the fraud detection contract specification in blockchain metrics. The proposed results indicate the practical viability of our scheme in real-financial CC spending and lending setups.

Automated summary

Credit card fraud has been addressed by using long short-term memory (LSTM) and explainable artificial intelligence (XAI). In this paper, a scheme named RaKShA is proposed to improve the performance of LSTM models by incorporating XAI. The proposed approach achieves high accuracy and stores data on the blockchain to ensure trustworthiness.