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This paper aims at presenting a graph-based risk assessment framework for the cryptocurrency wallet transactions, in the attempt to recognize wallets that are likely to pose high risks in decentralized networks. Data from blockchain and Neo4J’s graph database would model wallets as nodes and transactions as edges, hence allowing an analysis flow of transactions, thus identifying which wallets might be malicious. All these are integrated into a personalized risk rating algorithm. The high-risk nodes are raised by Cypher queries while visualizations aid in following illicit activities. The output of the experiment showed its efficiency in pattern detection involving fraud, assisting the regulator in improving security on transaction of cryptocurrencies.

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Crypto-Monitoring Tool

  • 章节

  • 发表日期：

    2026年

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  • Manikrao Dhore,
  • Sangita Maheshwar Jaybhaye,
  • Aryan Vimal,
  • Avish Agrawal,
  • Raghav Bajaj,
  • Rohan Barde

摘要

This paper aims at presenting a graph-based risk assessment framework for the cryptocurrency wallet transactions, in the attempt to recognize wallets that are likely to pose high risks in decentralized networks. Data from blockchain and Neo4J’s graph database would model wallets as nodes and transactions as edges, hence allowing an analysis flow of transactions, thus identifying which wallets might be malicious. All these are integrated into a personalized risk rating algorithm. The high-risk nodes are raised by Cypher queries while visualizations aid in following illicit activities. The output of the experiment showed its efficiency in pattern detection involving fraud, assisting the regulator in improving security on transaction of cryptocurrencies.