Payments Compliance Automation Confronts Growing Regulatory Complexity

Every time a person buys something online or transfers money through an application, there is a complex system working in the background to keep that transaction safe and legal. For e-commerce companies, banks, and payment platforms, this responsibility has become increasingly demanding. They are not just moving money, they are expected to stop illegal transfers, identify suspicious activity, and meet strict rules set by global regulators.

These obligations go far beyond preventing fraud. Organisations must ensure that no payments are made to sanctioned parties or used for activities like money laundering or terrorism financing. If they fail, the consequences can be severe. Financial authorities, including the U.S. Financial Crimes Enforcement Network (FinCEN), can impose heavy fines and penalties. For growing fintech and e-commerce businesses, getting compliance wrong can mean both financial and reputational damage.

This pressure has led many companies to turn toward automation to help manage compliance. But automation in payments is not as straightforward as it sounds. According to Udit Agarwal, a product manager who has worked on eCommerce and payment compliance systems at Google and Walmart, businesses face a tough question: how much can they rely on machines when the stakes are so high?

Agarwal explains that there are two main routes companies can take. Some buy ready-made compliance tools from providers such as Nice Actimize or Hummingbird. Others, especially larger organizations, build their own in-house systems. Custom solutions allow more control and flexibility, but they also require deep technical expertise and constant monitoring. “Many companies struggle with how much automation to introduce,” he says. “Too much, and you risk missing illegal activity. Too little, and you slow down the system and increase costs.”

During his tenure at Google, Agarwal led projects that directly supported multi-million-dollar businesses in meeting Anti-Money Laundering (AML) regulations. He provided tooling to automatically detect suspicious transaction trends, flagging and blocking potential bad actors in compliance with regulatory requirements. One of his key projects involved enabling Google Cloud to launch a new business model, which required meeting highly complex global compliance standards. His work didn’t stop there, he also improved internal tooling and precision detection systems that enhanced the efficiency and accuracy of suspicious activity monitoring.

The results of these efforts were significant. Agarwal’s initiatives unlocked new business opportunities by allowing divisions to operate confidently within regulatory frameworks. His tools not only strengthened payment integrity but also reduced manual oversight by automating the early detection and mitigation of potential risks. This balance of compliance assurance and operational efficiency reflects how AI and automation can directly contribute to business growth.

Still, the work is not without challenges. Optimizing the cost of managing false positives while maintaining full compliance remains one of the most difficult aspects of the job. Agarwal notes that achieving near-perfect accuracy, where every flagged transaction is a true positive,requires deep coordination between compliance and product teams, as well as continuous investment in smarter models. “It’s not just about finding bad actors,” he explains. “It’s about ensuring that legitimate users aren’t caught in the net. That’s the real test of intelligent automation.”

The challenge is particularly sharp in e-commerce, where every transaction involves both a buyer and a seller. This dual-party setup makes it easier for bad actors to disguise illegal transfers under legitimate trade. Companies can reduce this risk through pre-vetting of users and by running real-time checks based on factors like location and IP address. Still, no system can guarantee complete safety, especially as criminals use more sophisticated methods to hide their tracks.

Many leading technology and financial firms, including Google and Walmart, are now building compliance tools directly into their payment platforms instead of treating them as separate add-ons. This approach allows problems to be detected earlier and resolved faster, something Agarwal has championed through his work on scalable detection and automation frameworks.

Even with these advancements, full automation in compliance remains a distant goal. Regulators continue to emphasize the importance of human oversight, and companies are wary of relying solely on AI or algorithms for such sensitive work. However, Agarwal believes that progress in artificial intelligence and access to more reliable data could eventually change this. “With better data and smarter models, we can get closer to safe automation,” he says. “It’s about reducing risk without slowing down the business.”

The growing complexity of financial regulations means that companies cannot afford to treat compliance as a box-ticking exercise. It’s now central to how they operate and compete. As automation becomes more capable, the key will be using it carefully, not to replace human judgment but to strengthen it.

In the world of digital payments, trust is built quietly. It comes from systems designed by people who understand that technology must serve both convenience and compliance. The future of payment automation will depend not just on how fast systems can detect risk, but on how responsibly they are built to handle it.