Crypto Regulation Whiplash: What Insurers Should Do

Crypto had a strong 2025 in Washington—and that’s exactly why insurers should be nervous.

When enforcement eases, new products ship faster, more money moves through new rails, and adoption jumps. But when the rules are still half-written, the risk surface expands faster than underwriting, compliance, and claims teams can keep up. The Reuters-reported wins for the U.S. crypto industry—dropped lawsuits, looser bank posture, stablecoin rules, new SEC product approvals, and even a federal bitcoin stockpile—create a familiar dynamic for insurance: growth now, clarity later.

Here’s my stance: insurers that treat crypto as a niche “fintech” side quest will miss both the premium and the protection gap. Crypto is turning into payments infrastructure. That puts it squarely inside this series’ core theme—AI in payments & fintech infrastructure—because the same AI that secures card payments and ACH fraud can help insurers price, monitor, and pay crypto-related risk.

2025 made crypto easier to sell—2026 may make it harder to insure

Answer first: 2025 policy momentum increased crypto activity; 2026 uncertainty could increase loss frequency and severity.

The source article describes a year where regulators and policymakers signaled “yes” more often than “no”: accounting guidance was rescinded, high-profile lawsuits were dismissed, and a major law established federal rules for dollar-pegged tokens (stablecoins). The industry also pushed for market structure legislation—clarity on when tokens are securities vs. commodities—yet that’s still stalled.

For insurers, that combination is volatile:

• More mainstream usage means more insureds touch crypto indirectly (payroll, vendor payments, treasury, customer rewards, settlement, affiliate payouts).
• Incomplete market structure rules keep legal and compliance exposure high, especially for firms operating across multiple states and lines of business.
• Regulation by guidance can flip with administrations, which creates long-tail uncertainty—exactly the kind that breaks pricing models.

A simple way to translate the policy story into underwriting reality:

When regulation loosens before definitions are finalized, fraud scales faster than controls.

That’s not an anti-crypto argument. It’s a risk argument.

Crypto risk for insurers isn’t one risk—it’s five (and they stack)

Answer first: Crypto-related exposure shows up as stacked, correlated risks that traditional insurance silos weren’t designed for.

Insurers tend to bucket risks by product line: cyber, crime, D&O, E&O, K&R, property, marine/specie, surety. Crypto doesn’t respect those boundaries.

1) Payments fraud and authorization ambiguity

Stablecoins and token-based settlement can reduce some friction, but they introduce new disputes:

• Was a transfer “authorized” if an employee was socially engineered into approving it?
• Is wallet compromise closer to funds transfer fraud or computer fraud?
• How do you prove payee identity when the “account” is a wallet and the routing is a chain?

This matters because claims outcomes often hinge on definitions, logs, and timing. Crypto transactions are fast; dispute windows are not.

2) Custody and key management failures

A private key is a single point of failure. Loss scenarios tend to be binary: either you can sign, or you can’t. Traditional controls (password resets, account freezes) don’t map cleanly.

3) Regulatory and compliance blowback

The article highlights political and legislative uncertainty heading into 2026. For insureds, that translates to:

• higher legal spend
• changing licensing requirements
• enforcement risk if guidance shifts
• operational disruption when counterparties “de-risk” relationships

D&O and E&O don’t just attach to fraud. They attach to governance decisions made under unclear rules.

4) Market volatility and liquidity crunches

Even if an insurer isn’t covering “price moves,” volatility drives correlated events:

• increased fraud attempts during drawdowns
• vendor failures in crypto supply chains
• higher counterparty default risk
• liquidity events that trigger operational outages (and therefore business interruption claims)

5) Systemic concentration in a few providers

A small number of custodians, wallet providers, and infrastructure vendors sit behind many brands. That concentrates risk much like cloud providers do in cyber accumulation.

If you’re underwriting a portfolio, accumulation modeling becomes the real boss fight.

Where AI fits: underwriting, claims, and compliance that keep pace

Answer first: Insurers should use AI to monitor real-time signals, reduce manual review, and price crypto exposure dynamically—because static questionnaires are already obsolete.

“AI” here shouldn’t mean a chatbot on the website. It should mean pragmatic models and workflows that make the insurer faster and more consistent than the attacker.

AI use case #1: AI-driven underwriting for crypto-adjacent businesses

Most carriers still rely on long applications and point-in-time attestations. That’s weak when:

• wallet architecture changes quarterly
• vendors change frequently
• transaction volumes can spike overnight

A better approach is continuous underwriting with AI-supported signals:

• Entity resolution to map insureds to exchanges, custodians, wallet providers, and processors they use
• Vendor risk scoring using SOC reports, incident history, and control disclosures (normalized by AI)
• Behavioral baselines for transaction volumes and payout patterns (to spot operational risk)

Practical underwriting questions to operationalize with AI:

• What percentage of revenue touches digital assets?
• Are assets held in omnibus custody or segregated accounts?
• Is multi-sig enforced for treasury movements above a threshold?
• What’s the time-to-detect and time-to-contain for wallet anomalies?

AI doesn’t replace these questions—it turns them into measurable, monitorable variables.

AI use case #2: Fraud detection across stablecoin and crypto payment flows

As stablecoin rules mature, stablecoins will increasingly behave like payment rails. The fraud patterns will rhyme with card-not-present and ACH fraud:

• mule behavior
• synthetic identities
• rapid “test” transactions
• payout funneling to new wallets

AI excels at pattern recognition across high-volume events. For insurers, that can support:

• crime policy underwriting (controls validation)
• claim triage (is this pattern consistent with known fraud rings?)
• subrogation targeting (identifying negligent vendors or control gaps)

If you’re building or buying models, prioritize:

• graph analytics (wallet-to-wallet relationships)
• sequence models (how behavior changes before a theft)
• anomaly detection tuned for business context (payroll runs vs. vendor payouts)

AI use case #3: Regulatory compliance automation that reduces E&O and D&O exposure

The article flags market structure legislation as the “elephant in the room.” That’s not a political point—it’s an operational one.

When rules change, insureds struggle with:

• policies and procedures drift n- inconsistent screening or reporting
• training gaps
• documentation that doesn’t match what actually happened

AI can help by:

• summarizing regulatory updates into actionable control changes
• mapping obligations to internal controls (control libraries)
• flagging gaps in evidence collection (audit readiness)

This is especially relevant for insurers offering coverage to fintechs that touch crypto rails. Better compliance reduces loss frequency, and it reduces messy “who’s liable?” disputes.

What to do now: a 90-day playbook for insurers and brokers

Answer first: Treat crypto as payments infrastructure risk, update underwriting data, and build AI-assisted workflows before 2026 uncertainty hits.

Most teams don’t need a moonshot program. They need a tight plan with clear owners.

Step 1: Define “crypto exposure” consistently across your book

Create a taxonomy that underwriting, claims, and actuarial all share. For example:

• Direct: exchanges, custodians, token issuers, wallet providers
• Indirect: merchants accepting stablecoins, payroll firms, PSPs, marketplaces
• Incidental: companies holding digital assets on balance sheet

This avoids silent aggregation where crypto exposure hides inside “technology” class codes.

Step 2: Add 6 data points to your underwriting intake

These are low-friction but high-signal:

1. Digital asset revenue percentage
2. Custody model (self-custody vs. third-party)
3. Key management approach (HSM, MPC, multi-sig)
4. Transaction monitoring capability (in-house vs. vendor)
5. Incident response runbooks for wallet compromise
6. Top 5 critical vendors (custody, on/off-ramp, chain analytics, payments)

Then use AI to normalize and compare across submissions.

Step 3: Build an accumulation map (even if it’s imperfect)

Start with vendor concentration:

• which insureds rely on the same custodians
• which rely on the same payment processors
• which rely on the same cloud regions

AI helps by extracting vendor names from PDFs, SOC reports, and security questionnaires, then deduplicating them.

Step 4: Tighten claims playbooks for crypto disputes

Crypto claims often fail in the gray zones—authorization, timing, and evidence.

Operational improvements that pay back quickly:

• standardized evidence requests (wallet logs, signing policies, vendor tickets)
• rapid triage model to classify loss type (crime vs cyber vs professional)
• preferred vendor panels for forensics with wallet expertise

If your adjusters are learning wallet basics during a live claim, you’re already behind.

“People also ask” questions insurers are getting right now

Answer first: These are the questions I’d expect to dominate renewals as crypto rules evolve into 2026.

Is stablecoin risk more like banking risk or cyber risk?

It’s both. Stablecoins behave like payments infrastructure, but most loss events look like cyber/crime: credential theft, social engineering, vendor compromise, and operational errors.

Will clearer regulation reduce losses?

Clearer regulation reduces some ambiguity, but it also accelerates adoption. Net effect: fewer “what even is this?” disputes, but more volume to attack.

Can insurers price crypto risk without chain data?

You can start, but you’ll underprice outliers. The early win is AI-assisted vendor and control scoring; the next step is integrating transaction and behavioral signals where legally and contractually permitted.

What happens if the party “fizzles” in 2026?

A slowdown in legislative progress doesn’t mean crypto disappears. It means uncertainty lingers—and uncertainty is expensive.

Insurers are in a rare position: you see claims patterns across industries, and you can reward better controls with better pricing. The carriers and brokers that win in 2026 won’t be the loudest about crypto. They’ll be the most disciplined about data.

If you’re building capabilities in AI in payments & fintech infrastructure, this is a clean place to focus: use AI to connect underwriting, fraud signals, and compliance drift into one operating picture. That’s how you write crypto risk profitably instead of treating it as a headline.

Where is your organization most exposed right now—custody/key management, stablecoin payments fraud, or regulatory whiplash?