Norm Ai has raised $27 million in a Series A funding round to expand its artificial intelligence (AI)-powered regulatory compliance platform.
With this new capital, the company will continue to expand its platform and grow its client base, Norm Ai said in a Tuesday (June 25) press release.
“I’ve been conducting research at the intersection of AI and law for more than a decade,” John Nay, founder and CEO of Norm Ai, said in the release. “We are now at an inflection point in AI capabilities that, when properly harnessed, unlock massive improvements in regulatory compliance workflows across the economy.”
The company’s AI platform converts regulations into computer code and creates computer programs called Regulatory AI Agents that automate compliance analyses, making them more efficient, comprehensive and accurate, according to the press release.
This approach also makes it possible to integrate AI more deeply into businesses by providing a Regulatory AI Agent overlay that ensures AI-driven actions and generative AI content adhere to policies, the release said.
With these capabilities, Norm Ai helps companies accelerate their publication of highly regulated content, self-serve initial rounds of regulatory compliance reviews, and evaluate and finalize content in minutes rather than days, per the release.
Sri Viswanath, general partner at Coatue, which led the funding round, said in the release that Norm Ai aims to grow into “a transformational AI company.”
“Coatue continues to be impressed by Norm’s vision to deliver a comprehensive regulatory AI platform, and we are excited by the array of compliance workflows they are already able to support,” Viswanath said.
AI has emerged as a tool to address the challenges of different regulatory landscapes, offering solutions that enhance efficiency, accuracy and effectiveness in compliance management, PYMNTS reported June 13.
This technology can step in to replace traditional methods of compliance management that often fall short due to their reliance on manual processes and retrospective analysis.
In an earlier development in this space, Archer, a provider of risk management solutions, acquired Compliance.ai, a supplier of AI-driven regulatory change management solutions, in February.
Archer said at the time that the acquisition would empower its clients with AI technology to automate monitoring, tracking, reporting and responding to evolving regulations in real time.
For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.
Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
