The Blind Spot: Why Fraud Detection Keeps Failing

Fraud has evolved. But our defenses haven’t.

Despite years of investment in advanced analytics, anomaly detection, and machine learning, global fraud losses are projected to exceed $485 billion by 2025 (Juniper Research, 2022). Why?

Because most fraud detection systems are built to find what happened. Not why.

They ignore psychology. They ignore social context. They ignore the human element—the part that actually creates the fraud in the first place.

And that’s the blind spot.

The Psychology of Fraud: Missing in Action

Fraud is not a technical glitch; it’s a social act.

Dr. Donald Cressey’s classic “Fraud Triangle” theory (1953) identified three drivers of fraud: pressure, opportunity, and rationalization. Yet most detection tools only attempt to measure the second—opportunity—through anomalous access or irregular transaction patterns.

Psychologists like Dr. Dan Ariely (Behavioral Economics, Duke University) have shown that people don’t just defraud for survival; they do it when they can mentally justify it. In The (Honest) Truth About Dishonesty (2012), Ariely’s research revealed that most people cheat “just a little,” and that dishonesty is deeply tied to psychological framing and perceived social norms.

Yet today’s fraud engines don’t measure pressure. They don’t detect rationalization. They don’t model trust erosion or emotional volatility.

They detect noise. Not a motive.

The Case for Psychosocial Fraud Detection

At Fortza, we believe fraud detection must evolve from a transactional model to a behavioral one—one grounded in psychosocial insight.

Psychosocial fraud detection goes beyond thresholds and patterns. It uses NLP, user behavior analytics, and emotion modeling to detect:

• Changes in tone across chat and support channels
• Linguistic markers associated with deceit (Hancock et al., 2007)
• Shifts in usage patterns during periods of psychological distress (APA, 2020)

Our approach draws from social signal processing, a field defined by Pentland and Vinciarelli (2010), which uses AI to understand non-verbal cues in digital interactions.

In short, we’re building fraud detection that models how humans behave under stress, manipulation, or intent—not just when a flag flips red.

What the Industry Is Missing

Gartner’s 2023 Market Guide on Fraud Detection and Prevention noted that “vendors are oversaturating the market with anomaly detection tools that lack contextual awareness.” Meanwhile, a McKinsey report (2022) on digital trust found that consumer trust is eroding, and companies are under-leveraging behavioral data that could preempt attacks.

This is the 98% we’ve been ignoring.

The Stakes Have Changed

We are entering an era of AI-enabled fraud, where deepfakes, LLM-generated phishing, and real-time manipulation tactics are outpacing traditional defense tools.

Your fraud strategy can’t just detect the event. It needs to understand the human behavior that made it possible.

Join the Movement

This is the first in a series unpacking the psychology of fraud — and how Fortza is putting the human factor back into fraud detection.

Follow along. Let’s stop treating fraud like a data glitch — and start treating it like what it really is: a deeply human vulnerability.

References in the comments.

#FraudDetection #AI #Cybersecurity #BehavioralScience #Fortza #TrustDesign

References

• Cressey, D. R. (1953). Other People's Money: A Study in the Social Psychology of Embezzlement.
• Ariely, D. (2012). The (Honest) Truth About Dishonesty.
• Hancock, J. T., Curry, L., Goorha, S., & Woodworth, M. (2007). On Lying and Being Lied To, Discourse Processes.
• Vinciarelli, A., Pantic, M., & Pentland, A. (2010). Social Signal Processing: Survey of an Emerging Domain, Image and Vision Computing.
• Gartner (2023). Market Guide for Online Fraud Detection.
• McKinsey (2022). Digital Trust: The Competitive Advantage of the 2020s.
• APA (2020). Stress in America 2020: A National Mental Health Crisis.
• Juniper Research (2022). Online Payment Fraud: Emerging Threats, Segment Analysis & Market Forecasts 2022–2027.