AI Fraud Defense as Crypto Thefts Hit $3.4B

Crypto theft hit $3.4 billion in 2025 (January through early September), according to Chainalysis findings released Dec. 18. That number matters less as a headline and more as a signal: the attack surface around digital value is widening faster than most security programs can retool.

If you’re responsible for payments, fintech infrastructure, or fraud strategy, it’s tempting to treat crypto theft as “someone else’s problem.” That’s a mistake. The same tactics showing up in wallet drains and exchange breaches—credential theft, social engineering, malware, insider abuse, obfuscated money movement—are already being used against payment rails, merchant accounts, and payout systems. Digital assets just make the money movement faster, harder to reverse, and easier to launder.

This post is part of our AI in Cybersecurity series, and I’m going to take a clear stance: rules-only fraud programs won’t keep up with the scale and speed implied by $3.4B in theft. If your defenses can’t learn and adapt in near real time, you’re underwriting the attacker’s business model.

What the $3.4B crypto theft number really tells us

The direct answer: crypto theft at $3.4B indicates industrialized, repeatable attack operations—not one-off “hacks.” It also reflects a market reality: attackers follow liquidity, and crypto provides fast exit routes.

Chainalysis’ summary notes an uptick connected to North Korea-linked hacking activity. That’s not random. When theft is state-aligned or state-tolerated, attackers have time, funding, and patience. They’ll run long cons, build malware variants, and operate like a product team. Your fraud team, meanwhile, is trying to tune thresholds during a quarter-end freeze.

A few implications for payment and fintech infrastructure:

• Theft is increasingly multi-stage. Initial compromise often starts with identity theft, session hijacking, SIM swaps, or employee phishing—then escalates to fund movement.
• The goal isn’t just account takeover; it’s monetization. Crypto enables quick conversion, layering, and cross-chain movement.
• “Finality” is a feature for attackers. In card payments, you have disputes and chargebacks. In crypto, once assets move, recovery windows shrink dramatically.

Snippet-worthy reality: Crypto theft isn’t only a crypto problem—it’s a preview of how modern financial crime scales when transactions are fast, global, and hard to unwind.

Why digital asset risk is now payment infrastructure risk

The direct answer: as wallets, stablecoins, tokenized deposits, and crypto on/off-ramps integrate into mainstream finance, crypto security becomes part of payments security.

By December 2025, plenty of payment flows touch digital assets indirectly even if the consumer never sees “crypto” on a screen:

• On-ramps and off-ramps that convert fiat to digital assets (and back)
• Payout providers that support stablecoin settlement for cross-border speed
• Merchants using crypto payment processors (often for specific geographies)
• Treasury teams experimenting with stablecoins for working capital movement

That integration creates a familiar pattern: the fraud cost shows up where controls are weakest, not where the brand story is strongest. Attackers don’t care if your product is “fintech” or “crypto.” They care where identity proofing is thin, approvals are manual, logs are fragmented, and monitoring is lagging.

The new weakest link: authentication and approvals

The direct answer: most high-dollar losses still start with low-tech entry points.

In real incidents across financial services, the chain often looks like this:

1. Phished credentials or stolen session tokens
2. Access to admin panels, support tooling, or wallet management
3. Changes to payout destinations, withdrawal limits, or API keys
4. Rapid fund movement through multiple hops

If your system approves sensitive changes with “email + password + SMS OTP,” you’re betting your loss ratio on a channel attackers routinely compromise.

Money movement is where attacks become expensive

The direct answer: fraud prevention must focus on transaction and payout controls, not just login security.

Payments teams are used to optimizing conversion. The problem is that attackers love the same thing: low friction. The moment you offer instant payouts, real-time settlement, or automated withdrawals, you’ve created an express lane for theft.

Where AI fits: detection that adapts faster than attackers

The direct answer: AI-powered fraud detection is effective because it learns behavior patterns and surfaces anomalies that static rules miss.

Rules still matter. But rules alone struggle with:

• Novel attack paths (no rule exists yet)
• Fast-changing mule networks and beneficiary accounts
• Synthetic identities that look “normal” in isolation
• Coordinated attacks spread across many low-value events

AI is strongest when it’s used to connect weak signals into a confident decision. Think of it as building a “fraud narrative” from fragments: device posture, session behavior, beneficiary history, graph relationships, velocity patterns, and operational telemetry.

What “AI fraud detection” should mean in practice

The direct answer: it’s not a single model; it’s a layered system that scores risk continuously across the customer lifecycle.

A practical AI stack for payments and digital assets usually includes:

• Behavioral analytics: keystroke/mouse dynamics, session timing, navigation paths
• Device intelligence: emulator detection, rooted devices, mismatched OS/browser signals
• Identity risk scoring: document + selfie checks, watchlists, email/phone reputation
• Graph analysis: relationships among accounts, devices, IPs, beneficiaries, and merchants
• Anomaly detection for payouts: “is this withdrawal normal for this user right now?”
• LLM-assisted triage (carefully scoped): summarizing cases, extracting patterns from analyst notes, accelerating investigations

One opinion I’ll stand by: if your fraud system only scores at login, you’re defending the cheapest part of the attack. Score at every high-risk event—especially beneficiary add/change, API key creation, and withdrawals.

AI needs guardrails: explainability and controls

The direct answer: models must be measurable, auditable, and constrained—or they create operational risk.

Payments teams live in the world of disputes, compliance, and customer trust. So the AI program has to answer:

• Why was a transaction blocked (or allowed)?
• What features contributed most to the decision?
• How do we monitor drift and retrain safely?
• How do we prevent model exploitation (adversarial behavior)?

A useful standard: every high-impact decision should produce a human-readable reason code alongside a model score. Your analysts need to trust the machine, and your compliance team needs to defend it.

A practical blueprint: reducing theft across the money-movement lifecycle

The direct answer: the best results come from combining AI monitoring with “speed bumps” that activate only when risk spikes.

Here’s a blueprint that works in modern payment infrastructure, including crypto-adjacent flows.

1) Harden the highest-risk actions, not everything

The direct answer: protect the actions attackers need to cash out.

Focus controls on:

• Adding or changing payout beneficiaries
• Increasing withdrawal limits
• Creating API keys / changing webhook destinations
• First-time withdrawals to a new address/account
• High-velocity small withdrawals (testing behavior)

Use step-up verification that’s hard to phish (passkeys, device binding, authenticator apps, hardware keys) and require stronger approval for admin-level actions.

2) Treat addresses and beneficiaries like merchants (score them)

The direct answer: beneficiary risk scoring prevents “clean” accounts from paying “dirty” destinations.

Even if you don’t label anything “crypto,” the pattern holds: money leaves your system to somewhere. Score destinations using:

• Age and history of the beneficiary
• Network connections (shared devices, shared IP ranges, shared employer domains)
• Past dispute/fraud associations
• Velocity of new destination creation across the platform

3) Add AI-driven anomaly detection to payout rails

The direct answer: payout anomaly detection catches fraud that bypasses identity checks.

Identity proofing helps at onboarding, but many losses happen months later. Your model should ask:

• Is the amount abnormal relative to this user’s baseline?
• Is the timing abnormal (new time zone, unusual hour, post-password-reset)?
• Is the device/context abnormal?
• Is this part of a broader campaign (many accounts behaving similarly)?

When risk spikes, apply controls like delayed settlement, queued withdrawals, or manual review—selectively, not universally.

4) Use real-time monitoring plus post-event hunting

The direct answer: you need both: stopping fraud live and finding what slipped through.

Real-time decisions reduce immediate loss. Post-event hunting finds patterns, compromised clusters, and control gaps.

A strong weekly operating rhythm looks like:

• Daily: monitor alert quality (false positives, missed fraud)
• Weekly: hunt for new attack paths and update features/rules
• Monthly: review model drift, threshold calibration, and control performance

Common questions teams ask (and straight answers)

“If theft is in crypto, why should my payments team care?”

Because attackers reuse playbooks. Crypto theft highlights the most profitable money movement routes, and those routes increasingly overlap with payouts, cross-border transfers, and instant settlement.

“Will AI increase false positives and hurt conversion?”

Not if you deploy it correctly. The point is precision: fewer blunt rules, more context-aware decisions. Pair AI scoring with tiered friction so low-risk traffic stays low-friction.

“What’s the first thing to implement if we’re behind?”

Start with payout protection: anomaly detection on withdrawals and beneficiary changes, plus step-up auth for high-risk actions. That’s where losses concentrate.

What to do next if you want fewer fraud losses in 2026

Crypto theft reaching $3.4 billion in 2025 is a loud message for anyone building modern money movement: the attackers are professional, well-funded, and fast. The teams that win aren’t the ones with the longest rule lists—they’re the ones with adaptive detection, tight controls on cash-out paths, and strong operational cadence.

If you’re planning your 2026 roadmap, treat AI in cybersecurity as a core part of payments infrastructure, not a side project. Put AI-powered fraud detection where it counts: identity, behavior, and—most of all—payouts.

What would change in your risk posture if every beneficiary change and withdrawal had a real-time risk score, clear reason codes, and an automatic “pause and verify” option when the pattern looks wrong?