As our CEO Chaitanya Sarda puts it: "The notion of a company was always something like a store or a factory. But now you have individual creators, contributors on different platforms, people working from home with their own LLC... Almost everybody will have a business entity sooner or later."
He expects the number of businesses on the planet to quadruple, or quintuple, in the next five years. For compliance teams, that changes the job entirely as KYB becomes a throughput problem. The manual workflow most teams are running simply wasn't built for a world where a TikTok creator or a freelance contractor is as likely to be onboarding as a traditional business. The strain shows up in a familiar place: more signals than anyone can act on (registries, documents, sanctions lists, website content, adverse media), and no clean path from data to decision.
AI agents are starting to close that gap. Specialist systems each handle one job end to end and hand back something structured that an analyst can review and act on. In practice, the teams doing this well start with agents that handle the groundwork, like gathering signals, summarizing findings, flagging anomalies. Importantly, these teams ensure that humans keep ownership of every final decision, making accountability an inherent trait from the start. That’s particularly important because a workflow that cannot be explained and defended is a liability in compliance, regardless of how efficient it is. The goal is automation with a clear audit trail, not automation instead of one.
Regulators are arriving at the same conclusion. More on that below.
Read more about The Future of the Compliance Desk here.
Singapore has long been one of the more sophisticated financial compliance environments in the world. But what makes it worth paying attention to today is not just the quality of its framework, but the direction it’s moving in.
In November 2025, the Monetary Authority of Singapore published proposed Guidelines on AI Risk Management for all financial institutions, setting out formal supervisory expectations on governance, life cycle controls, and responsible AI deployment across the sector. Then, earlier this month, MAS went a step further. It announced an active collaboration with five banks, the Government Technology Agency of Singapore, and the Singapore Police Force to build AI and machine learning models for pre-emptive financial crime detection.
The message from MAS echoes what FinCEN has been signaling in the US. Earlier this year, FinCEN proposed formal guidance recognizing AI as a legitimate tool in KYB and AML compliance, provided institutions can demonstrate governance, explainability, and human accountability in how it is used. For compliance leaders operating across both markets, that convergence matters. The expectation is no longer that you will eventually adopt AI. It is that when you do, you can demonstrate it was done responsibly.
Read more about what FinCEN's proposed rule means for KYB and AML here.
1. When you talk to compliance leaders, how do they describe the problem on their desk right now. Is the issue usually that they don't have enough data, or that they can't act on what they already have?
The challenge today is less about whether data exists and more about whether teams can operationalize it fast enough and confidently enough. Compliance leaders are dealing with a new generation of businesses that don’t always fit traditional onboarding models: creators, online sellers, remote contractors, global SMBs, all operating across fragmented digital footprints.
What we hear consistently is that the problem is no longer a single source of truth. Teams are trying to reconcile what a business says it does, what a registry says it is and what its actual online presence suggests. Those mismatches create friction, manual reviews and repeated RFIs that slow onboarding down significantly.
The most forward looking teams are using AI agents to help triangulate those signals upfront, pulling together registry data, website intelligence, documents, sanctions screening and behavioral indicators into something analysts can review and act on quickly. That doesn’t remove human judgment. It gives teams a cleaner and more structured starting point for making decisions.
2. We talk about AI agents handling the repetitive work so humans can focus on judgment and accountability. When you explain that model to a compliance leader, what's the reaction and what's the hardest part for them to believe?
Most compliance leaders are already bought into the idea that AI will become part of the workflow. The hesitation is less about whether it will happen and more about where the boundaries are. The biggest question usually comes down to accountability. Teams want to understand where human oversight remains necessary, how decisions can be explained to regulators and whether the system can produce a defensible audit trail when something goes wrong. What’s interesting is that the conversation has evolved quickly. A year ago, many teams were asking whether AI belonged in compliance at all. Today, the discussion is much more operational: where can agents reliably handle repetitive work and where should humans stay directly involved?
My view is that human oversight will remain fundamental, particularly around edge cases, escalation paths and risk decisions. But as institutions become more sophisticated in how they define rules, risk appetite and customer segmentation, agents will increasingly handle the groundwork that consumes compliance teams today.
3. Regulators from FinCEN to MAS are now explicitly endorsing AI in compliance. Does that shift the conversation you're having with buyers, or are most teams still waiting to be told they have to change?
It has absolutely shifted the conversation. Regulatory endorsement matters enormously in compliance because it changes AI from something experimental into something institutions can seriously put into play. When regulators like MAS and FinCEN acknowledge that AI can play a legitimate role in compliance workflows, provided governance and accountability are in place, it gives teams confidence to move from exploration into implementation.
At the same time, there’s also a practical reality driving urgency. Compliance teams are already struggling with increasingly manual and fragmented workflows while onboarding volumes and complexity continue to rise. The pressure is coming from both directions: regulators are becoming more open to responsible AI adoption, while operational demands are making traditional processes harder to sustain.
That combination is what’s accelerating adoption. Most teams are no longer asking whether AI will become part of compliance operations. They’re trying to determine how to implement it responsibly before the volume problem becomes unmanageable.
