Knowledge extraction from financial network data for fraud and risk detection

Financial fraud is a significant threat to financial institutions and the financial regulatory body because fraudulent transactions are difficult to detect and trace. The proposed research paper presents a viable artificial intelligence model for deriving knowledge from financial network data to support fraud and risk detection. This paper introduces a node-level financial risk scoring framework with precision-driven optimization tailored for regulatory fraud detection environments. The method combines graph-based structural characteristics and machine learning models to detect suspicious transactions and concealed network characteristics related to fraud. An Elliptic node-level risk indicator based on structural influence derived from network analysis of the Elliptic Bitcoin transaction data was used to build the predictive models. Results show that Node2Vec +XGBoost XGBoost and LightGBM have AUCs of 0.952, 0.945 and 0.938, respectively. Besides, LightGBM has high precision (0.975) and a very small false-positive rate (0.0013). These findings indicate that the framework can identify illegal transactions and reduce false alarms under the regulations. In addition, one uses graph insights to determine structurally powerful nodes that act as intermediaries in problematic transaction networks. Explainable Artificial Intelligence (XAI) can also increase transparency by allowing financial authorities and compliance groups to obtain risk ratings. Overall, the given framework demonstrates that the combination of graph analytics and explainable machine learning has the potential to facilitate the identification of financial fraud, risk monitoring, and regulatory decision-making in the contemporary financial landscape.