Babel Street announced advancements to its Identity Risk capabilities centered around accurately determining whether different records represent the same person or entity.
Organizations operating in high-stakes environments like global sanctions screening, border security, financial crime compliance and law enforcement investigations are faced with imprecise identity data, the company said.
They choose between overly broad matching floods teams that produce false positives and manual reviews, and overly rigid matching that can lead to lapses in security oversight. And as AI agents are integrated into the screening, vetting and investigative process, the challenge is exacerbated.
“Agents can operate at machine speed, but the decisions they make are only as trustworthy as the identity data and scoring logic beneath them,” Babel Street said in its announcement. “Accurate, explainable identity resolution is critical to whether automated risk intelligence can be relied upon.”
The company’s latest enhancements address this by improving how organizations match names, addresses, dates and full records across more than 20 languages and scripts.
The three advancements improve identity matching through regional name intelligence, consolidate duplicate and variant records into a single trusted identity, and give teams field-level control over match scoring. Together, the capabilities are designed to reduce false positives, prevent inflated risk scores and improve confidence in global identity data.
“A single missed match can result in a severe compliance breach, while a flood of false positives can paralyze an entire team,” said Gil Irizarry, chief innovation officer at Babel Street.
“As organizations deploy agentic workflows, they cannot afford to run high-speed AI on noisy or fragmented data. These updates give security and compliance teams the exact precision, control, and data hygiene required to make confident, defensible decisions at scale.”
The new capabilities are available for enterprise and government deployments.