In an era where criminals use generative AI to forge documents, mimic identities, and exploit financial systems at scale, fraud is no longer a low-tech hustle. It’s a high-tech arms race. Now, Resistant AI, a Prague-based startup, has raised $25 million in Series B funding to fight fire with fire—using artificial intelligence to outsmart AI-powered fraud.
The round was led by DTCP Growth, with participation from Experian, GV (Google Ventures), and Notion Capital. The funding will power Resistant AI’s product expansion, deepen its threat intelligence capabilities, and scale its operations across Europe.
From Cybersecurity to Financial Crime: A Founder Who’s Seen It All
Martin Rehak, CEO and co-founder of Resistant AI, is no newcomer to the AI security landscape. He earned a PhD in Artificial Intelligence from Czech Technical University and co-founded Cognitive Security, a network security startup acquired by Cisco in 2013. There, he led global threat analytics initiatives before spinning out with his original team to found Resistant AI in 2019.
“The core of our founding team has worked together for nearly 20 years,” Rehak told us. “We’ve been building AI-driven solutions to tackle security challenges since 2006—long before AI became the buzzword it is today.”
While many fraud prevention platforms grew out of compliance or KYC needs, Resistant AI’s origins are deeply rooted in cybercrime analytics—giving it a nuanced understanding of adversarial behavior.
Native AI for Native Threats
Resistant AI’s platform is built on a multi-model, native AI architecture that integrates signals across a client’s risk tech stack—from document scans and transactional flows to behavioral and identity signals. This architecture is designed not only to detect, but to contextualize and prevent complex financial crimes like:
Synthetic identity fraud
Authorised Push Payment (APP) scams
Money muling networks
AI-generated document forgeries
Synthetic corporate identities
Unlike legacy fraud systems that rely on rule-based detection or siloed models, Resistant AI fuses data signals in real time—delivering higher precision and lower false positive rates.
“We don’t just know what catches criminals—we know what doesn’t,” Rehak said. “And that knowledge is what enables us to always stay one step ahead.”
A Market Ready for Machine Learning–First Defenses
The startup is tackling what it calls a “financial crime stack gap”: legacy systems designed for simpler fraud now struggle against adaptive, AI-driven attacks. Resistant AI aims to augment fraud and compliance teams with tools that detect anomalies across documents, transactions, and behaviors—all within seconds.
Rehak adds: “Fraud and financial crime used to be a backwater. That’s changed dramatically with two trends: the industrialisation of fraud networks and the arrival of generative AI.”
This shift has opened the floodgates for attackers who can now automate identity creation, use deepfakes to bypass onboarding, and manipulate documents at scale. Resistant AI positions itself as the real-time immune system financial institutions need to survive.
Capital for Capability, and Expansion
With the $25 million infusion, the company plans to:
Accelerate product development: Enhancing document forgery detection, real-time transaction monitoring, and scalable threat intelligence.
Scale European operations: Establishing deeper market presence across the EU, where demand for fraud prevention is growing amid tightening regulation.
Strengthen partnerships: Embedding its platform into existing compliance and anti-fraud ecosystems to reach more banks, lenders, and fintech players.
The funding follows its earlier $16.6 million Series A, which was also led by GV, bringing its total funding to over $40 million to date.
Competition Is Growing—but So Is the Urgency
Resistant AI is not without competition. Key players like Onfido, Ocrolus, ComplyAdvantage, and Featurespace offer their own takes on identity verification and fraud detection. But Resistant AI’s focus on native integration across signals, rather than add-on modules or siloed analysis, gives it a defensible edge.
Its competitive moat lies in its technical DNA: a deep bench of cybersecurity and machine learning experts who treat fraud as an evolving threat model—not a compliance checkbox.
Editorial Perspective: From Niche to Necessity
What was once a niche corner of financial services—fraud analytics—is rapidly becoming a core strategic function. As fraudsters industrialise attacks with AI, institutions are being forced to replace static, rules-based systems with agile, intelligent detection platforms. Resistant AI’s opportunity lies in being not just a product, but a platform layer across the modern risk stack.
Its technical pedigree and cybercrime roots give it credibility where it matters. In a world where the “signal” in fraudulent behavior changes daily, adaptability is everything. Resistant AI’s multi-model architecture allows it to learn faster than legacy tools—and that’s where long-term value lies.
If it can continue to demonstrate measurable ROI in fraud loss reduction and false positive suppression, it stands a real chance of becoming a category leader, not just a feature vendor.
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