We know digital payment systems are growing faster all over the world, and as they grow, they have some consequences. One big problem among them is digital payment fraud, which rapidly increases as payment systems grow. Fraudsters can surpass rule-based detection systems as they adapt; they have new patterns for doing fraud. They find loopholes in the main architecture from where they manipulate data and do fraud. We study both problems and reach a very solid solution to track down all fraud patterns. We added artificial intelligence and the Hyperledger Fabric blockchain, which is used to detect the pattern of fraud, and a blockchain, which is used to make the payment system tamper-proof. All data related to the payment system are stored in a single system, which is very secure and not able to be encrypted. The detection system runs on four methods. For unstable workflow it uses LSTM networks. For rule-based classification, we used a random forest classifier. For fraud detection, we used a GraphSAGE network, and last, for any suspicious activity, we used an autoencoder. All the things are watched by a meta-learner, which analyzes and combines their output and provides data to trigger a smart contract response, which works automatically. Different financial institutions are used to train their systems without using shared row transaction data to make privacy learn their module detection. We concluded our study, but two public benchmarks are set by PaySim (6.35M transactions) and IEEE-CIS (590K transactions). In PaySim we succeed with up to 98.3% accuracy and an AUCROC of 0.991. Adversarial robustness testing shows the team requires 3.2 times larger to prevent any mistake for success for a single model. These results show much need of AI and blockchain. Using AI and blockchain is very efficient; they are better than anything else to detect fraud.
Avinash et al. (Thu,) studied this question.