Heka raises $14M Series A to tackle AI-fueled fraud epidemic | CTech

Heka, an Israeli startup using web intelligence to tackle the growing epidemic of digital fraud, has secured $14 million in Series A funding to expand its operations in the United States and deepen its presence across the UK and Europe.

The funding round was led by Windare Ventures, with participation from Barclays, Cornèr Banca, and other institutional backers.

Consumer fraud losses reached $12.5 billion last year, a 38% year-over-year jump, driven by disposable burner accounts, fake digital profiles, and increasingly sophisticated AI-generated content. Many of the traditional fraud-prevention tools, from credit bureau reports to transaction velocity models, were built for an era when static files and predictable user behavior were enough to catch bad actors. Heka claims it was founded to close that gap.

Founded in 2021 by Rafael Berber, a former Global Head of Equity Trading at Merrill Lynch; Ishay Horowitz, a senior officer in the Israeli intelligence community; and Idan Bar-Dov, a fintech and high-tech lawyer, Heka draws inspiration from intelligence tradecraft to provide what it calls “always-on, explainable” web intelligence. Its proprietary AI engine analyzes vast amounts of publicly available online data to build real-time digital profiles, surfacing alias use, reputation risk, and behavioral anomalies that can help financial institutions spot fraud and connect with legitimate customers faster.

At its core is an analyst-grade AI agent that works in the wild, unlike legacy tools that rely on static blacklists and stale credit files. In a recent deployment with a global payment processor, Heka says its engine detected 65% of account takeover losses without disrupting healthy customer activity, a key metric for financial institutions wary of turning away legitimate clients.

The company says its technology is already generating millions in revenue through partnerships with banks, payment processors, and pension funds. Clients use Heka’s data for fraud detection, account management, recovery, and other sensitive decisions where old data sources fall short.