Taktile has raised $54 million to help power its artificial intelligence (AI)-powered risk management tool.
The Series B round, announced Thursday (Feb. 27), brings the company’s total funding to $79 million, according to Balderton Capital, which led the round. The company says its funding comes at a moment when mainstream automation for high-stakes decisioning is on the cusp of a breakthrough.
“In financial services and other regulated industries, the stakes are high, and every decision matters,” the announcement said. “Established institutions face intense pressure as AI-driven FinTech startups rapidly innovate, challenging their market share and margins. However, many enterprises struggle to adopt AI at scale.”
Among the main obstacles, Balderton continued, is a shortage of engineers with the skills to develop and maintain AI systems. There is also a need for greater precision, as even the most advanced AI large language models can manage only specific aspects of complex problems instead of offering “fully reliable solutions.”
Taktile “closes this gap by equipping risk teams and their engineering counterparts with a shared platform to build, manage and optimize complex AI-powered workflows and agents that are governed by rules and embedded into business logic,” Balderton added.
The announcement also notes the consequences of wrong decisions — loan defaults, fraud losses and compliance fines — pointing to TD Bank’s $3.1 billion payment last year in connection to its anti-money laundering failures in the U.S.
“From day one of our journey, we believed that millions of lives could be improved by enabling organizations to make optimal decisions for their customers,” said Maik Taro Wehmeyer, Taktile’s co-founder and CEO.
“By keeping experienced risk experts in control, we make it possible for even the most regulated businesses in financial services to fully adopt AI into high-stakes workflows.”
PYMNTS looked at AI’s role in risk management and fraud detection last month in a conversation with Mark Sundt, chief technology officer for Stax Payments.
“The biggest red flags we encounter are merchants with newly established banking relationships or websites. These temporal attributes often signal fraudulent intent,” he said.
Sundt also described suspicious patterns in transactional fraud, like large transactions followed by batch reversals or refunds sent to different credit cards.
“These scenarios demand robust AI systems to detect and mitigate fraudulent activities at scale,” he told PYMNTS.
Source: https://www.pymnts.com/
