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@article{196874,
        author = {CHUNDURU KESAVA MOHAN GANESH ANIRUDH and CHANDALURI SAI MALLIKARJUN and ACHANTI NAVEEN KUMAR and GUDA YASWANTH and K.THRILOCHANA DEVI},
        title = {INDUCTIVE GRAPH NEURAL NETWORKS FOR ANTI-MONEY LAUNDERING A TOPOLOGICAL APPROACH TO FRAUD DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3991-3996},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196874},
        abstract = {Financial fraud and money laundering schemes have evolved into complex, interconnected networks that traditional tabular analysis methods consistently fail to uncover. Standard machine learning classifiers evaluate transactions as isolated data rows, rendering them blind to the structural dependencies between accounts that define modern financial crime. This paper proposes a robust fraud detection framework using an Inductive Graph Neural Network (GNN), specifically GraphSAGE (Graph Sample and Aggregate), applied to the Elliptic Bitcoin dataset. The dataset comprises 203,769 transactions represented as a directed graph where nodes correspond to wallet addresses and edges represent fund flows. The model is trained on a strict temporal split time steps 1–34 for training and 35–49 for testing to simulate real-world concept drift and prevent data leakage. To address severe class imbalance (~2% illicit nodes), an inverse-frequency class-weighted cross-entropy loss function is employed. The proposed system achieves an Illicit-class Recall of 65.65% and F1-Score of 0.507, outperforming a Random Forest baseline by over 21 percentage points in Recall. A Streamlit dashboard with Explainable AI (XAI) reasoning and interactive PyVis topology maps enables real-time compliance use.},
        keywords = {Anti-Money Laundering, Financial Fraud Detection, Graph Neural Networks, GraphSAGE, Inductive Learning, Blockchain Forensics, Explainable AI, Temporal Validation.},
        month = {April},
        }