There is a question that sits in the back of every bank risk officer’s mind, and it rarely gets answered with any precision: how good is our fraud management program, really? Not compared to last quarter’s numbers. Not compared to internal targets set before the current threat landscape looked the way it does. But compared to what the best institutions in the industry are actually achieving right now, in 2026, facing the same fraud environment that everyone else is navigating.
That question matters more than ever because the fraud environment has shifted dramatically. Banking fraud losses have reached unprecedented levels globally. In the United States alone, consumer fraud losses topped $12.5 billion in 2024, representing a 25% increase year over year. Internet crime losses exceeded $16.6 billion in the same period. Generative AI, the same technology enabling chatbots and content creation, is now a significant tool in the hands of fraudsters, enabling deepfakes, synthetic identity creation, and AI-powered phishing attacks at scale. Deloitte has projected that generative AI-enabled fraud could reach $40 billion in the United States alone in the years ahead.
Against this backdrop, Feedzai, the global leader in AI-native financial crime prevention, has launched a new benchmarking report designed to give banks something they have never had in a comprehensive form: a clear, objective framework for evaluating where their fraud management performance stands relative to industry peers.
Why Benchmarking Fraud Management Is Harder Than It Sounds
Most industries have mature benchmarking frameworks. Revenue per customer, cost per acquisition, customer satisfaction scores: these are standardized enough that any company can quickly understand whether they are above or below industry average and by how much. Fraud management has historically resisted this kind of standardization, for a few reasons.
First, fraud data is commercially sensitive. Banks do not typically publish their detection rates, false positive rates, or fraud loss ratios in ways that allow genuine peer comparison. Second, the diversity of banking models, from retail to commercial and from community banks to global institutions processing trillions of transactions, makes direct comparison difficult without proper normalization. Third, fraud tactics change so rapidly that benchmarks from even six months ago may reflect a threat environment that no longer exists.
Feedzai’s position in the market makes it uniquely suited to address these challenges. The company’s RiskOps platform processes over 1.6 trillion transactions per year on behalf of clients representing 1.6 billion consumer accounts across 190 countries. This scale of data creates a longitudinal view of fraud patterns, detection rates, and institutional performance that no individual bank can achieve on its own, and it forms the foundation on which the new benchmarking report is built.
What the Benchmarking Report Covers
The report gives financial institutions structured visibility into the fraud management metrics that matter most: detection accuracy, false positive rates, case investigation efficiency, scam prevention outcomes, and the overall effectiveness of AI model performance in real-world deployment.
For banks operating AI-powered fraud detection systems, false positive rates are a particularly important benchmarking dimension. False positives, which are legitimate transactions flagged as fraudulent, create direct operational costs through case investigation workloads and create customer experience damage through declined transactions and friction-laden authentication requests. Research has consistently shown that modern AI platforms are capable of reducing false positive rates by 35 to 50% compared to rule-based systems, with corresponding improvements in fraud detection rates of 25 to 40%. Understanding where a bank sits on those spectrums, relative to the best-performing institutions using comparable technology, is actionable intelligence.
The report also addresses scam prevention outcomes, which is an increasingly critical dimension as regulators around the world move to hold financial institutions more directly liable for authorized push payment fraud. Under the UK’s Payment Services Regulator framework, banks are required to split scam losses with the receiving institution. Under PSD3 in the European Union, liability for bank impersonation scams is shifting to financial institutions. Australia’s Scam Prevention Framework is expanding cross-sector liability. Understanding how well a bank’s current scam detection capabilities perform, compared to institutions that have invested heavily in behavioral biometrics and real-time intervention tools, is essential context for those regulatory conversations.
The AI Performance Gap Among Banks
One of the more important findings from Feedzai’s broader research program, which surveyed 562 financial professionals and has been tracking AI adoption trends in the sector, is that while 90% of financial institutions now use AI for fraud detection, there is enormous variation in how effectively that AI is deployed and maintained.
Data management remains the single biggest challenge. Roughly 87% of banks cite fragmented data sources and regulatory constraints as their primary hurdles to effective AI deployment. The quality of AI fraud detection depends almost entirely on the quality of the data feeding it. Institutions that have not invested in the infrastructure to unify transactional, behavioral biometric, device, and network data into a coherent customer risk profile are running AI models that are significantly less effective than they could be.
There is also the question of explainability. Around 89% of banks in Feedzai’s research say they prioritize transparency and explainability in their AI systems, driven partly by regulatory requirements and partly by the practical need for fraud analysts to understand and trust the decisions the system is making. Institutions that have invested in Responsible AI infrastructure, including bias quantification and plain-language decision explanations, are consistently outperforming those operating black-box models that analysts struggle to interrogate.
The new benchmarking report gives banks the structured context to understand these gaps, not as abstract best practices, but as specific performance differentials tied to measurable outcomes.
The Competitive and Regulatory Stakes
For bank executives making investment decisions about fraud technology, the benchmarking report addresses a long-standing challenge: justifying the cost of better fraud management against outcomes that are inherently probabilistic. How much does it cost to go from a 72% detection rate to an 85% detection rate? What is that improvement worth in avoided losses, reduced investigation costs, and customer retention?
The bank fraud prevention market is moving toward a model where the best-performing institutions will have measurable competitive advantages in customer acquisition and retention. Customers who experience fraudulent transactions they were not protected from do not stay with the same bank. The regulatory environment is also moving toward a model where institutions that cannot demonstrate proactive scam prevention capabilities will face direct financial liability for losses they could have prevented.
In that context, a rigorous benchmarking framework for financial crime prevention is not a compliance exercise or a nice-to-have reporting capability. It is an essential strategic tool, and Feedzai’s launch of this report reflects a genuine understanding of where the industry’s most pressing analytical need currently lies.
References:
- Feedzai.com, 2025 AI Trends in Fraud and Financial Crime Prevention Report
- Feedzai.com, Fraud Prevention Solutions Overview, April 2026
- ArticlesLedge, AI Fraud Detection in Banking Complete Guide 2026
- Feedzai Blog, 2025 Fraud Prevention Trends Year-End Scorecard, December 2025
- Custom Market Insights, Global Enterprise Fraud Management Market 2026-2035
