15 anti-fraud startups investors are backing for 2026

Key Takeaways

• European compliance funding surged 226% to €9 billion, driven by AMLD6 and PSD2 regulations
• AI-powered fraud detection and real-time KYC verification dominate investor interest
• Startups combining behavioral biometrics with machine learning are winning enterprise deals

European anti-fraud startups raised €9 billion last year, a 226% jump from the €720 million logged in the previous period. Sifted polled investors across Europe's fintech ecosystem to identify the 15 compliance startups they expect to break out in 2026. The list spans identity verification, transaction monitoring, and AI-driven risk scoring, reflecting where capital and regulatory pressure are converging.

The timing isn't accidental. AMLD6, the EU's latest anti-money laundering directive, tightened requirements on financial institutions and created fresh demand for third-party compliance tools. UK-based banks and fintechs face their own regulatory updates through 2026, adding fuel to the sector.

Why investors are piling into compliance

"The most innovative, best-of-breed, and most comprehensive anti-fraud and anti-money-laundering solutions haven't reached the mainstream yet," said Max Rofagha, partner at Fintech Collective. "Adoption will accelerate into 2026 and beyond."

Jessica Holzbach, partner at 0TO9 Fund, pointed to a separate dynamic. She described a "pendulum" effect: enterprises that previously built in-house fraud systems are now looking to buy from startups because internal tools can't keep pace with AI-generated attacks. Generative AI has made fraud cheaper and faster to execute. Deepfakes for identity spoofing and synthetic identities for account opening are both on the rise.

That pressure shows up in deal flow. Several investors told Sifted they're seeing more inbound from banks and payment processors actively seeking modern fraud stacks.

Which startups made the list

Sifted's roundup includes companies tackling different slices of the fraud problem. A few names stand out for their technical approach or traction.

Salv, based in Estonia, offers a collaborative intelligence platform that lets financial institutions share fraud signals across borders without exposing raw customer data. The company claims its network approach can cut false positives by 74% while catching 99% of actual fraud, compared to roughly 28% on a standalone system.

Resistant AI specializes in document fraud, using machine learning to spot manipulated invoices, payslips, and identity documents. Banks use it upstream in onboarding flows to catch forgeries before accounts open.

Sardine takes a behavioral approach. It monitors device signals, typing cadence, and session patterns in real time to flag account takeovers and bot attacks. Clients include Mercury, Brex, and FTX's bankruptcy estate.

Other names on the list include Flagright, which offers an API-first AML platform aimed at neobanks, and Lucinity, an Icelandic company using explainable AI to reduce alert fatigue for compliance analysts.

What investors are looking for

Tim Wanders at Motive Partners said his firm prioritizes startups that combine real-time decisioning with explainability. Regulators want to understand why a transaction was blocked. Black-box AI doesn't cut it.

Chris Gottschalk at Mouro Capital emphasized distribution. "The best product doesn't always win. It's about who gets embedded into banking infrastructure first." He pointed to startups integrating directly with core banking vendors like Mambu, Temenos, and Thought Machine as having an edge.

Several investors flagged unit economics as a concern. Fraud detection often prices on transaction volume, which can create margin pressure as clients scale. Startups with platform pricing or consortium models may have more defensible revenue.

The AI arms race in fraud

Fraudsters now use the same large language models that power legitimate products. One investor described seeing phishing campaigns generated entirely by AI, complete with personalized context scraped from LinkedIn. Voice cloning for vishing attacks is another growth area.

The startups gaining ground use AI defensively. They train models on millions of legitimate and fraudulent interactions, then deploy them to score risk in milliseconds. Some offer hybrid systems that combine machine learning with rules engines, letting compliance teams tune thresholds without waiting for model retraining.

For founders building in adjacent spaces, the takeaway is clear: compliance infrastructure is now a budget line item, not a cost center to minimize. Banks and fintechs are willing to pay for solutions that reduce fraud losses and satisfy regulators.

What this means for founders outside fraud

If you're building a fintech product, identity verification and transaction monitoring are table stakes. The startups on this list are potential partners or integration points. Several offer APIs designed for quick deployment. Sardine, for instance, can run as a single JavaScript snippet in a checkout flow.

For B2B SaaS founders outside fintech, the broader signal is that compliance-driven software categories are expanding. Data privacy (GDPR, state-level US laws), AI governance, and ESG reporting all follow similar dynamics: regulation creates demand, enterprises need third-party tools, and startups with the right timing can capture durable revenue.

Frequently Asked Questions

Which anti-fraud startups are investors watching for 2026?

Sifted's investor poll highlighted Salv, Resistant AI, Sardine, Flagright, and Lucinity among 15 startups. Each tackles a different fraud vector, from document forgery to behavioral biometrics.

Why is European compliance funding growing so fast?

AMLD6 and PSD2 regulations created new compliance requirements. Banks and fintechs are buying third-party tools rather than building in-house, driving demand for startups.

How do AI-powered fraud detection startups work?

They train machine learning models on large datasets of legitimate and fraudulent activity. The models score transactions in real time, flagging anomalies for review or automatic blocking.

What should fintech founders look for in a fraud prevention vendor?

Prioritize explainability (regulators want audit trails), integration speed, and pricing that scales with your business. Run a proof-of-concept on real data before signing.

Is the anti-fraud startup market oversaturated?

Not yet. Investors say adoption of best-of-breed solutions is still early. Most banks rely on legacy systems. The shift to modern, API-first platforms is just beginning.

Source: Sifted