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Resistant AI announced a $25 million Series B on October 13, 2025, led by DTCP Growth, with existing investors Experian, GV and Notion Capital participating. The Prague-founded company says it will use the capital to expand its document-fraud detection and transaction-monitoring products into new markets, develop partnerships and build threat-intelligence capabilities. The announcement did not disclose the company’s valuation, investor stakes or a detailed spending plan.
What is known about the Series B
The financing is a $25 million Series B led by DTCP Growth. Experian, GV (formerly Google Ventures) and Notion Capital also took part as existing investors, according to Resistant AI’s announcement. October 13, 2025, is the announcement date; public details cited here do not establish that the round closed on that date.
The company did not disclose a valuation, dilution, individual investor contributions or whether the financing was entirely equity, debt, or a combination. Its stated plans are to take document-fraud detection into new territories, expand transaction monitoring through markets and partnerships, and build threat intelligence. It also described the funding as supporting its ambition to become a profitable European AI company.
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Published accounts differ on how much the company has raised in total. SecurityWeek reported more than $55 million, while the law firm advising Resistant AI on the round put the figure at $53 million. The earlier Series A was $16.6 million, announced in 2021. Given the discrepancy, the most cautious summary is that public accounts put total funding at at least $53 million; a definitive reconciliation has not been disclosed.
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What Resistant AI sells
Founded in 2019, Resistant AI is a financial-crime-prevention company headquartered in Prague, with reported team locations in London and New York. Founder Martin Rehak is its CEO. The company’s founding team previously created Cognitive Security, which Cisco acquired in 2013; Resistant AI’s Series A announcement says that company’s technology became the basis for Cisco’s Cognitive Threat Analytics platform.
Resistant AI describes two main product areas. They address related risks, but not the same task:
- Resistant Documents is designed to assess documents submitted during customer onboarding, know-your-customer (KYC) checks, lending and underwriting, insurance applications, merchant or business verification, and account opening. The company says it detects forged, manipulated, synthetic and AI-generated documents.
- Resistant Transactions is a transaction-monitoring product intended to supplement existing rules-based controls with machine-learning models. The company says it targets money laundering, money muling, account takeover, authorized push-payment fraud, synthetic corporate identities, serial fraud and emerging patterns.
In practical terms, document analysis asks whether evidence submitted to establish an identity or business has been altered or fabricated. Transaction monitoring looks for suspicious behavior in payments and account activity. A provider offering both may help institutions connect risks across parts of the customer lifecycle, but success in document checks does not by itself establish performance in transaction monitoring, or vice versa. Resistant AI positions its products as additions to an institution’s existing risk systems, not replacements for its core platforms.
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Why institutions are looking at this category
Financial firms face pressure to make onboarding and payment decisions quickly while examining more digital applications and transactions. Generative AI can help fraudsters produce more convincing documents, while faster digital processes may leave less time for manual review. At the same time, fraud can span identity evidence, account behavior and payments, making it harder to spot when controls are isolated in separate systems.
Machine-learning tools can help identify patterns that fixed rules miss, but they do not make fraud detection automatic or error-free. Rules remain useful for clear, auditable controls; statistical models can add signals about less familiar behavior. Both need monitoring, and the combination can produce duplicate alerts or conflicting scores if systems are poorly integrated.
Resistant AI also framed its funding announcement around the use of AI agents in financial-risk operations. The company argues that large language model-based agents can have weaknesses in quantitative risk analysis, hallucination and adversarial security. Those are the company’s stated concerns, not independent measurements of the performance of all AI agents. More broadly, using AI to help investigators does not remove the need for human review, controls on automated actions and clear accountability.
Traction and performance claims
Resistant AI reported that its customer base had grown fourfold and annual recurring revenue (ARR) tenfold since its Series A, and that transaction-analysis volume had increased 100-fold. It also said it had verified more than 150 million documents by the time of the October 2025 announcement. Its press-release archive later reported more than 200 million documents analyzed as of July 2026. These are company-reported cumulative or growth figures, not independently audited performance results.
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The company lists Dun & Bradstreet, Payoneer, Close Brothers, PennyMac, AXA, Anna Money, Finom and Bank of Valletta as customers. That is a company-reported customer list; it does not establish which product each organization uses, the scope of any deployment or an endorsement by every named organization.
Resistant AI also says customers have achieved a 3× increase in document-fraud prevention, five-times-faster review times, 90% automation rates and a fivefold increase in second-line analyst productivity. The announcement does not define the baselines or measurement methods, provide sample sizes or customer-level results, or say whether these figures are averages or selected examples. They should be treated as vendor claims, not guaranteed outcomes for a prospective buyer.
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The company said it was breakeven in September 2025, but did not specify whether that meant monthly, quarterly or run-rate breakeven, or provide an accounting definition. It reported more than 100 employees in the October 2025 announcement, another company-reported figure that may change over time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the funding may mean for buyers
The round gives Resistant AI capital to pursue the expansion it has described, but funding alone does not show that its products outperform competing tools. Institutions evaluating this kind of software should test it against their own fraud mix, countries, document types and operational constraints. Useful questions include:
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Coverage: Which document types and fraud patterns can it assess? Which transaction risks are in scope, and where does another product or internal control remain necessary?
- Performance: What are the false-positive and false-negative rates on representative data? How do detection and review-time claims compare with a controlled baseline?
- Integration and speed: How does the system connect with onboarding, payments, case management and existing transaction-monitoring tools? Can it respond within the institution’s required decision time?
- Explainability and oversight: Can investigators understand and document an alert? What can be automated, when is human review required, and how can staff challenge or override a decision?
- Governance and resilience: How are models validated, updated and monitored for drift? How does the provider respond to new evasion tactics and manipulated inputs?
- Data handling: Where is data stored and processed, how long is it retained, and how are identity data and cross-border transfers managed?
A specialist fraud platform may offer deeper analysis in its area, while a broader identity or risk platform may reduce the number of vendors and integrations. Combining document and transaction signals can strengthen a risk view, but it also raises data-governance and privacy questions. Buyers should verify that performance holds across their populations and markets, and that added automation does not create an unmanageable alert queue or opaque decisions.
Resistant AI’s raise is part of competition across identity verification, fraud orchestration, payment-risk, anti-money-laundering and transaction-monitoring software, as well as tools institutions build internally. The available information does not establish the company’s market share or support a claim that it is a market leader.
What remains undisclosed
Beyond the round amount, lead investor and participants, public information does not provide the valuation, ownership changes, revenue, detailed geographic rollout, customer-by-customer deployment results or a precise allocation of the new capital. The company has said it will build threat-intelligence capabilities, but the announcement does not detail their scope. Its claimed operating and performance metrics have not been independently validated in the sources cited here.
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