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@article{205094,
        author = {Rushikesh  Tokle and Karan Shingade and Akshay Walunjkar and Sanskruti Pawar},
        title = {CryptoMapAI: A Machine Learning-Based System for Fraud Detection in Cryptocurrency Transactions using ChainGuard-V1 Engine},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5219-5226},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205094},
        abstract = {The surge in cryptocurrency adoption has redefined digital finance, introduced decentralization and transparency but also facilitated illicit activities such as drug trafficking and money laundering. The pseudonymous nature of digital assets like Bitcoin, Ethereum, and Monero poses major challenges for law-enforcement agencies in tracing the real identities behind suspicious wallet activities. This research proposes CryptoMapAI, an AI-driven framework designed to trace cryptocurrency trans-actions to their final destinations and uncover hidden criminal networks. The system integrates blockchain data from public and privacy-centric networks with external intelligence sources including darknet markets and flagged wallet databases. Using machine learning, graph analytics, and recursive clustering, CryptoMapAI identifies obfuscated transaction flows across mixers, tumblers, and cross-chain transfers. The model employs Random Forest and anomaly detection algorithms to classify suspicious activities and visualize the complete transaction net-work through an intuitive dashboard. Experimental results demonstrate a detection accuracy of 99%, enabling efficient identification of high-risk wallets and transaction patterns.},
        keywords = {Artificial Intelligence, Blockchain Forensics, Cryptocurrency Tracing, Money Laundering Detection, Graph Analytics, Machine Learning},
        month = {June},
        }