Fraudsters are using readily available software to run card testing, scams and synthetic identity fraud across many transactions.
Banks are combining transaction foundation models, graph neural networks and existing machine-learning systems to detect behavioral and network patterns.
Agentic AI can gather evidence and prepare fraud investigations, but human analysts should retain authority over case decisions and SAR filings, according to AWS’ Paul Chang.
Fraud attacks can now be assembled with commercially packaged software, distributed across accounts and devices, and altered faster than banks can rewrite a conventional fraud rule.
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“We’re seeing fraudsters leverage commoditized SaaS toolkits sold on dark web marketplaces to orchestrate scams, card testing attacks and synthetic identity fraud at industry scale,” AWS Head of Payment Networks Paul Chang told PYMNTS.
Traditional rule-based systems are insufficient for detecting the range of attacks banks are seeing.
“Banks are really responding by moving beyond static rules towards adaptive AI-driven defenses that use machine learning to score transactions in real time, learn from emerging fraud patterns and continuously retrain models as attack vectors shift,” he said.
One of the new components is the transaction foundation model, or TFM. Rather than examining only the characteristics assigned to an individual payment, these models can learn from long sequences of financial activity.
Chang described TFMs as “large-scale AI models pre-trained on billions of financial events,” which can include payments, mobile banking activity, marketing data and product interactions.
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