Trusted financial data for agentic commerce: A multi-agent framework for credit risk, fraud detection, and secure payment decisions

Financial institutions increasingly require intelligent risk-management systems that combine predictive performance with data reliability, interpretability, and adaptable decision policies for emerging agentic commerce environments. This study proposes a Trusted Financial Data (TFD) Multi-Agent Framework that integrates predictive risk modeling, behavioral intelligence, data-trust assessment, explainable artificial intelligence, and uncertainty-aware decision orchestration for credit-default and payment-fraud management. The framework translates heterogeneous risk and trust signals into interpretable Approve, Review, and Decline decisions using fixed and adaptive decision policies. Experimental evaluation was conducted on the Home Credit Default Risk and IEEE-CIS Fraud Detection datasets against five machine-learning baselines: Logistic Regression, Random Forest, XGBoost, LightGBM, and Multi-Layer Perceptron. Adaptive TFD achieved recall values of 0.6234 and 0.9366, with ROC-AUC values of 0.7584 and 0.9474 on Home Credit and IEEE-CIS, respectively. Adaptive orchestration consistently improved minority-class risk detection relative to fixed TFD policies, although at the cost of increased false-positive interventions. SHAP-based global and local explanations further enhanced transparency into model predictions and decision behavior. Overall, the findings demonstrate the potential of TFD as a trust-aware multi-agent decision-orchestration layer that combines predictive, behavioral, uncertainty, and data-quality signals to support configurable, explainable, and adaptive financial risk management across heterogeneous operational environments.