The State of Fraud in 2025: Why AI Is No Longer Optional

Introduction

Fraud didn't just tick up in 2025. It went corporate.

Organized rings now share playbooks, copy what works, and scale attacks across institutions. According to a recent 2025 market analysis conducted by The Harris Poll, most decision-makers saw more incidents than last year. But what worries them most isn't just the dollar loss, it's the reputational damage.

The volume is up, the adversaries are coordinated, and customer trust is on the line. The real question isn't whether to use AI anymore. It's how you use it to stay ahead.\

The industry reality: by the numbers

Based on a 2025 industry survey of US decision-makers, we see a clear transformation in the threat landscape.

The data reveals a critical gap:

• 99% of institutions already use AI in prevention
• 71% say organized rings are responsible for most attacks
• The Gap: Only 33% detect fraud at onboarding, while most (56%) catch it at the transaction level

That's the gap. By the time fraud hits a transaction, the fraudster already has a trusted account. You aren't preventing; you are cleaning up.

The 2025 reality: three trends you cannot ignore

1. Professionalized adversaries

Think of fraud rings like franchises. They develop a playbook, test it at one institution, then roll it out across others. They share tools and copy what works. This explains why institutions can increase spending on controls and still see fraud climb.

2. The battlefield is digital

With 80% of fraud now occurring in online or mobile banking channels, risk is concentrated in apps, browsers, and compromised devices. The most effective strategies are now layering identity, document, and device signals during onboarding rather than relying solely on transaction monitoring.

3. AI cuts both ways

Attackers are leveraging AI to scale synthetic identities and manipulated documents. The advantage isn't coming from AI adoption alone. It's coming from explainable decisions and continuous model updates.

Looking to 2026: three AI-led shifts

Investment plans for the next twelve months show where prevention will move first. These are industry-wide trends, not product claims, here's what leading risk programs are prioritizing:
Caption: Investment Priorities, Next 12 Months (Industry Data, 2025)

Data shows that 64% of organizations plan to prioritize investment in identity risk solutions, followed by document verification (49%).

Shift 1: Identity-first risk at onboarding

Too many detections still happen late. The direction is clear: move prevention earlier.

What a modern onboarding stack looks like:

• Document forensics and liveness: Template detection, image checks, selfie/voice verification when risk justifies friction
• Device risk: Fingerprinting, emulator detection, SIM-swap signals
• Lists and relationships: Sanctions, PEP screening, link analysis to known bad accounts
• Behavioral signals: Velocity checks, copy-paste patterns, repeated routes, mule indicators

Shift 2: Explainable AI that you can defend

Adoption is widespread. The next differentiator is explainability. Industry analysts expect regulatory changes in 2026 to drive explainability requirements, particularly as scam liability regulations expand globally.

What explainability looks like in practice:

• Reason codes in business language, not just scores
• Evidence records and audit traces: Signals that influenced decisions, immutable logs of who, when, why
• Reviewer guidance for analysts to confirm or reverse decisions quickly

Shift 3: From point tools to unified risk platforms

Tool sprawl makes it hard to see the whole picture. Organizations are consolidating around platforms that bring identity, document, device, and behavior into a single view.

What unified risk platforms enable:

• Single view of identity and fraud risk across products
• Risk-based orchestration instead of vendor-by-vendor policies
• Cross-product deduplication of people and devices
• Built-in metrics and playbooks, not custom reporting

The Scoreboard: What leaders are tracking

Leading risk programs focus on outcomes, not vanity metrics:

• Fraud per 1,000 applications: Tracking decline without gaming thresholds
• False positives at onboarding: Ensuring these drop while fraud stays controlled
• Time-to-decision: Optimizing for automated vs. manual flows
• Account takeover per 10k MAU: Establishing a monthly active user baseline

Conclusion

The pattern is clear: fraud went corporate in 2025, with organized rings driving volume and digital channels concentrating risk. But the response is evolving. Teams are moving prevention earlier, demanding explainability from their AI, and consolidating platforms to see the full picture.

The advantage won't come from adding more friction or more tools. It will come from better decisions at the right moments, powered by unified data, clear reason codes, and models that keep pace with how fast fraud tactics evolve.

Organizations that treat fraud prevention as a system, not a checklist, will be the ones that stay ahead.

How we think about it at DeepXL

Understanding these industry trends is exactly why we built DeepXL the way we did. While the trends above represent the market struggle, our approach is designed to be the solution.

We built our approach around three priorities to close the gaps identified in the 2025 outlook:

API-First integration: We allow teams to layer AI-powered detection into existing flows without rebuilding infrastructure.

Explainable AI by default: Every decision includes confidence scores and visual evidence. We believe you should always be able to defend a call when customers or regulators ask "why."

Specialized defense: We focus specifically on the threats that hit fintech and insurance hardest; synthetic identities and manipulated documents.\

About the data

Industry statistics are sourced from 2025 surveys conducted by The Harris Poll. Outlook for 2026 reflects trends observed across fraud intelligence research, trust and privacy analysis, and industry adoption studies. Analysis and recommendations are DeepXL's independent point of view.