A Fraud Detection System for Reducing Blockchain Transaction Risks using Explainable Graph Neural Networks
Open AccessWith the emergence of blockchain in financial applications, it is critical to assess and manage the financial risks of fraudulent events to protect customers, minimize loss, and prevent malicious activities. Unlike traditional credit payment systems, immutable blockchain systems cannot dispute charges, and investigating illicit transactions could be resource-consuming. However, conventional rule-based methods are inefficient or brittle and uninterpretable for monitoring and managing blockchain transaction risks.This praxis incorporates artificial intelligence into blockchain risk management by discriminating malicious blockchain transactions and acquainting risk managers with precise and granular information in the risk management and decision-making processes. Bitcoin transactional data is extracted and processed for malicious transactions detection using a collection of machine learning methods. The comprehensive model evaluation and comparison are conducted to select the best solution. Key drivers and explanations are provided to better understand the reason behind the discrimination. This praxis enhances blockchain risk management by leveraging advanced graph neural networks with an explainable technique to recognize malicious blockchain transactions.
- All rights reserved
Notice to Authors
If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.