Stephanie Spangler

Stephanie Spangler writes about AI-powered fraud detection, document risk, and how financial institutions are adopting agentic AI. Her work focuses on translating complex fraud detection technology into practical guidance for risk teams — covering topics like AI-generated document fraud, explainable AI, agentic workflows, and what fraud teams should look for when evaluating detection tooling.

Product marketing in fraud detection sits at the intersection of technical capability and buyer need. It involves understanding how fraud detection systems work — the signals they surface, the workflows they support, the regulatory requirements they serve — and communicating that clearly to the fraud analysts, risk managers, and compliance teams who rely on those systems. At Inscribe, that means translating developments in agentic AI and document fraud detection into content that helps financial institutions make informed decisions about their fraud prevention stack.

AI-powered fraud detection has shifted from static, rules-based systems toward agentic workflows — platforms where AI agents plan investigations, cross-reference signals, and explain their conclusions rather than simply outputting a score. At the same time, the threat landscape has shifted: generative AI has made it faster and cheaper to produce convincing fake documents, meaning fraud is no longer rare or isolated but routine and scalable. The most effective fraud detection platforms today are those that can reason through novel fraud patterns and produce audit-ready explanations for every decision.