The Role of Agentic AI in Fraud Detection: From Reaction to Anticipation

Fraud remains a significant operational threat for organizations handling vast volumes of data and transactions, particularly in sectors like healthcare, logistics and retail. As methods grow more sophisticated and harder to detect, the financial and reputational impact escalates. In this context, next-generation artificial intelligence, specifically Agentic AI, is transforming how companies address this risk. 

The Association of Certified Fraud Examiners reports that fraud costs organizations approximately 5% of their annual revenue, amounting to over $3.1 billion in losses based on analysed cases (“2024 Report to the Nations”). KPMG adds that e-commerce and investment scams are among the most financially damaging, often only discovered months after the fact, when recovery is no longer possible and the damage is done (“Global Banking Scam Survey”). 

AI has emerged as a promising solution, but expectations are falling short. A recent MIT Technology Review x Boomi report (“A Playbook for crafting AI strategy”) shows that while 95% of firms are experimenting with AI, fewer than 6% have successfully scaled it. The blockers are not model quality, but leadership gaps, legacy infrastructure, and unclear return on investment. 

Too often, AI initiatives remain in pilot mode, focused on tools like chatbots or copilots. While useful, these implementations rarely deliver the resilience and autonomy needed for high-risk areas like fraud. What’s required is a system built and trained to make decisions, in real time and with full context. 

That’s where Agentic AI comes in. 

From Insight to Intelligent Action 

Agentic AI introduces a new operational model for decision-making. These intelligent highly specialized and trained agents operate autonomously, analyse real-time data, adapt continuously, and respond instantly to emerging threats - not in hours or days, but milliseconds! 

Traditional fraud prevention systems are often rule-based, flagging suspicious patterns like duplicate billing, wrong data or high-value transactions. But fraud today is subtle and distributed: a manipulated discount here, a false invoice there. On their own, these actions appear harmless, but together they signal coordinated fraud. Agentic AI acts as a network of smart sensors, identifying these patterns as they emerge and executing predefined responses. 

EFFY applies this in practice across healthcare, retail and other business environments. For example, in healthcare, agents validate clinical data against billing records to detect anomalies such as policy abuse, phantom claims or upcoding. In retail, AI agents identify unusual payer behaviour, abusive return action or loyalty fraud, operating alongside existing systems and workflows. 

Fraud as a Strategic Use Case for Agentic AI  

Fraud is constantly evolving in complexity and context. That makes it one of the most demanding and high-impact use cases to validate the real-world effectiveness of Agentic AI.  

While Agentic AI is designed for autonomy and real-time action, it’s important to note that a human-in-the-loop approach remains essential for a fraction of cases. Human oversight ensures continuous learning and training and helps prevent errors in ambiguous or novel situations, validating and maintaining the reliability and trustworthiness of AI-driven decisions. 

At EFFY, our focus is not only into delivering efficiency solutions that generating alerts to be reviewed later but also to enable real-time orchestration of decisions that protect operations at the exact moment risk is detected. This shift, from retrospective analysis to proactive actionable analytics, redefines how businesses mitigate risk, prevent fraud, ensure compliance, and maintain operational continuity and financial sustainability. 

The organizations that will lead in this space are the ones capable of aligning AI to business-critical processes, embedding intelligence where decisions are made, and driving outcomes through context-aware automation.  

As fraud becomes increasingly machine-enabled, only organizations that pair advanced AI with human insight will stay ahead of the threat.