0100489646

open access

Accepted Manuscript

Permissioned Blockchain-Based XGBoost for Multi Banks Fraud Detection

English (英語)

Neural Information Processing

683-692

Springer

2023-04-13

2024-05-29

Fraud detection is one of the financial institution problems which can utilize Machine Learning (ML). However, the fraud activity is hard to detect since the occurrence is relatively low compared to the actual transaction. Several banks can collaborate to gather more fraudulent transactions from their data. However, the collaboration can cause data leakage from each bank, where the customer data should be confidential. Decentralized ML is one of the approaches to tackle the privacy-preserving aspect. This work proposed a fully decentralized environment using a permissioned blockchain to detect multiple banks’ fraud. The training process utilizes a continual eXtreme Gradient Boosting (XGBoost) model. We provided the architecture of blockchain implementation for multiple banks, where it is conducted as batch and streaming data processing. As we compared our approach with the centralized, individual, and federated GBDT models, it maintains a good prediction performance and fulfills the environment of a fully distributed system.

Decentralized model

会議発表論文

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG