Ethics-Aware Hybrid Graph–Neuro-Symbolic Framework for Adaptive Fraud Management in Large-Scale Financial Systems

Abstract

Fraud management systems are becoming more and more based on data-driven artificial intelligence processes to identify anomalous and malicious behavior in massive digital ecosystems. Nevertheless, the current solutions are usually characterized by low flexibility, inability to be interpreted, and such ethical limitations like bias, biased decision-making, and low transparency. This paper aims to tackle these issues by introducing a hybrid ethics-aware framework of fraud management relying on Graph Neural Networks (GNNs), Neuro-Symbolic Reasoning, and Reinforcement Learning (RL) as one framework. The suggested model assumes transactional environments to be dynamic graphs, which allows GNNs to efficiently learn the relation pattern and identify coordinated fraudulent patterns that are challenging in conventional feature-based models. In order to increase transparency and ethical adherence, domain-specific rules, regulatory requirements and principles of fairness would be directly inserted into the decision maker with a neuro-symbolic reasoning layer. Moreover, the limited reinforcement learning subsystem is continuously refining the policies of fraud detection in response to changing fraud methods, and it optimizes the risk thresholds, being explicit about avoiding ethical risks such as discriminatory results and excessive false positives. Empirical studies of benchmark fraud data sets show that the hybrid framework proposed can much better detect fraud, be resistant to concept drift, and explain decisions compared to traditional machine learning and deep models. Moreover, it is stated that ethical performance indicators are more favorable, and the framework is appropriate to be applied in actual financial and digital security systems. The findings reveal the promise of ethics-based hybrid AI systems as a dependable and trustworthy solution to fraud management in the new generation.