The Real Cost of an Outdated Fraud Strategy 

Fraud losses aren’t slowing down — they’re compounding.  

• Unauthorized-party fraud now accounts for 71% of all fraud incidents and dollar losses at U.S. financial institutions, driven primarily by credential theft and account takeovers.  
• Average fraud loss rates have risen to 0.8 basis points industry-wide, with large banks absorbing losses more than four times that figure.  
• And half of all institutions report that fraud is directly hurting customer loyalty — not just the bottom line.  

These findings, from PYMNTS Intelligence’s 2025 State of Fraud and Financial Crime report, paint a clear picture: the threat is evolving faster than most defenses. 

Fraud has always been a cat-and-mouse game. What’s changed is that the mouse now has machine learning, operates at payments speed and learns from every interaction. Financial institutions and fintechs that haven’t kept pace aren’t just losing money—they’re losing customers and competitive ground.  

The tension we hear consistently from clients: fraud doesn’t just cost you losses and chargebacks. It costs you trust, and trust is what keeps accounts open. 

Balancing Precision with Detection 

There’s a quiet cost most fraud teams and customers feel every day: false positives. Reducing false positives in fraud detection is one of the toughest balances an institution has to strike. 

As faster payment methods gain popularity, the pressure on accurate detection only intensifies. Customers expect every legitimate transaction to be approved instantly. They expect convenience at a very high level: fast transactions, low friction, and seamless experiences. According to PYMNTS Intelligence, nearly 1 in 3 cardholders who experience a false decline will use that card less frequently or stop using it altogether. 

When fraud detection models are too rigid or operating without sufficient context, they don’t just block bad transactions—they block good ones. A declined payment at checkout, a blocked transfer, a card that fails when a customer needs it. 

It’s a delicate balancing act. Too much friction and cardholders put your card at the back of the wallet—or leave altogether. Too little and fraud losses climb.  

The goal isn’t simply to catch more fraud. It’s to catch it with precision that keeps legitimate customers transacting. The goal isn’t to eliminate friction entirely. It’s to add it in precise moments where it’s needed, with an easy exit for customers when the transaction turns out to be legitimate. A two-way text that asks, “was this you?” and lets the cardholder respond yes in seconds is friction that feels like protection, not inconvenience. That’s the difference between blunt vs. precise controls. 

Rules Got You Here. They Won’t Get You There. 

For years, financial institutions have built fraud strategies like a set of guardrails. Write a rule, set a threshold, flag what looks off. It worked when fraud moved slower, and patterns held. 

That approach is no longer sufficient because rules-based systems can only catch what they’ve already seen. Today’s fraud is dynamic. Threat actors probe authorization thresholds, identify behavioral gaps, and scale what succeeds. They’re actively mapping your rule library, not constrained by it. 

Institutions need to honor a frictionless customer experience while trying to stop fraud at the same time. Fraud teams can no longer focus solely on fraud rules creation. Balance is the key. 

🎥 WATCH: i2c Says Complacency, Not Fraud, Is the Real Threat 

The Real-Time Infrastructure Gap 

The real divide isn’t between institutions that have fraud controls and those that don’t. It’s between those running fraud detection in payment processing systems as a parallel process and those running it as an embedded, real-time authorization process. 

Fraud platforms built on disconnected systems—where authorization decisions, transaction monitoring, disputes and behavioral analytics live in separate tools—create fragmented data views that undermine model accuracy.  

When fraud detection is separated from the transaction authorization layer, the fraud model sees what happened, not what’s happening. That delay, even measured in milliseconds, is where exposure lives. 

AI Works When It’s Inside the Decision 

AI fraud detection in banking isn’t a solution by itself. Applied poorly—analyzing batch data after transactions have already processed—it adds latency without value. 

🎥 WATCH: Financial Institutions Shift into High-Gear with AI Fraud Detection 

Applied well, it changes everything. Embedded directly into authorization decisioning and trained continuously on live transaction and behavioral data, AI enables fraud models to adapt as tactics evolve—without requiring manual rule updates for every new threat vector.  

The model architecture matters here.  

A gradient boosted tree model (the approach powering our fraud detection) works like a skilled angler. Instead of casting a wide net across the entire pond, it studies the water, picks the most promising spots and drops the line precisely where the big fish are most likely to bite. 

The result is a model that gets more precise with every transaction it processes. Delivering a 40% fraud capture rate with only 0.5% customer friction, we designed our award-winning fraud models to stop fraud without stopping customer success.  

Case in point: Earlier this year, Mastercard analyzed the i2c-processed portfolio of a Latin American digital bank client, comparing authorization, fraud and chargeback performance against the Central American and Caribbean regional benchmark to identify scalable opportunities.  

Guided by i2c’s strong best practices, the bank demonstrated above-benchmark, best-in-class performance, achieving 87% approval rates while delivering fraud performance 3.5x better than regional peers. 

“i2c demonstrates solid alignment with global best practices across its business model, operations, technology, and governance,” Mastercard Advisors noted, “with strengths in fraud detection processes and AI-driven capabilities.” 

📕 MUST-READ: Hybrid AI-Human Models Sharpen Fraud Response 

Fraud Strategy Is Now a Growth Lever 

This balanced approach is what institutions should be demanding from their payment fraud prevention systems. 

Leading institutions realize fraud management is no longer purely defensive. Done well, it’s a growth enabler. Preparation comes down to agility: the real-time intelligence to respond as conditions change, not after they already have. 

“Any platform that can’t respond dynamically to these risks is going to fall behind,” said Matt Pearce VP of Fraud Risk Management & Dispute Operations in a recent PYMNTS article. “The institutions gaining ground are acting faster, with richer context, at the exact moment of authorization. It’s why we build fraud and risk directly into the platform, so every authorization decision is made with full transactional and behavioral context—velocity, device and channel signals, spending pattern anomalies—evaluated in real time, not reconciled afterward.” 

When you approve more legitimate transactions and stay ahead of emerging threats, you’re not just protecting revenue—you’re delivering a better experience than competitors still declining transactions out of caution.  

Fraud isn’t a problem you solve once—it’s a capability you build continuously. If your payments platform isn’t embedding fraud in the authorization decision, learning in real time and delivering precision at scale, you’re not just behind—you’re exposed.  

Performance Check: Key Questions Answered 

Why is fraud harder to manage today than it was five years ago? 

Fraud now operates at payments speed, adapting in real time to detection patterns and exploiting gaps in systems built for a slower environment. Instant payments infrastructure has accelerated both legitimate and fraudulent transaction velocity—leaving less time to make accurate decisions. 

What’s the fundamental shift in how fraud should be approached? 

The shift is architectural: from fraud detection as a parallel, post-authorization process to fraud intelligence embedded directly into the authorization decision, where it has full transactional context and can act within the moment that matters. 

How does platform infrastructure affect fraud outcomes? 

When fraud detection, transaction monitoring and behavioral analytics are unified in a single platform rather than reconciled across separate systems, models have access to complete, real-time data. That translates directly into more accurate decisions and fewer false positives. 

Where does AI make the most meaningful difference in fraud? 

AI delivers the most value when it’s embedded in live decisioning—learning continuously from transaction and behavioral data to detect novel fraud patterns, reduce false positives, and adapt without requiring manual rule intervention for every new threat. 

How should financial institutions and fintechs think about fraud investment going forward? 

As a continuous capability, not a compliance checkbox. The institutions gaining competitive advantage are those whose fraud platforms evolve with the threat landscape in real time—protecting revenue, preserving customer experience and enabling growth simultaneously. That’s exactly what we’re building toward at i2c.