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@article{203596,
        author = {Prof R. N. Devray and Sneha Sharad Bhapkar and Renuka Santosh Kardile and Rutuja Narayan Mote and Vaishnavi Dnyandev Wani},
        title = {ChainGuard: AI-Powered Money Laundering Detection over Blockchain Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11903-11909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203596},
        abstract = {Money laundering has become increasingly complex with the rise of digital payment systems and blockchain-based financial platforms. Traditional Anti-Money Laundering (AML) frameworks rely mainly on static rules and manual investigation, which often fail to identify evolving fraudulent behaviors and generate high false-positive rates. To address these limitations, this research proposes BlockDetect, an intelligent AML detection framework that integrates machine learning techniques with blockchain transaction analytics. The system extracts behavioral and structural features from blockchain data, analyzes wallet interactions, and classifies transactions based on their likelihood of being suspicious. Machine learning algorithms, such as Random Forest and XGBoost, are employed to detect anomalous patterns commonly associated with layering, structuring, and circular money flows. The approach enhances accuracy, reduces manual effort, and provides an interpretable decision-support mechanism for AML investigators. The study demonstrates that combining ML-based anomaly detection with blockchain transparency can significantly strengthen financial security and support early identification of illicit activities. The proposed framework serves as a scalable and adaptable solution for modern AML challenges in decentralized financial environments.},
        keywords = {BlockDetect, Anti-Money Laundering, Machine Learning, Blockchain, Data Classification, Python, Fraud Detection, etc.},
        month = {May},
        }