For years, fintech’s ambition centered on full automation. In the paycard ecosystem, that vision has been realized. AI is now capable of executing a majority of operational decisions like issuing cards, adjusting limits as well as detecting and preventing fraud.

But as transaction volumes scale into the billions, threats become more sophisticated and regulatory scrutiny intensifies, a paradox has emerged. Autonomy requires more human oversight, not less.

In the paycard ecosystem, risk is not an abstract concern. Manju Kumari, Senior Fraud and Risk Manager at TaskUs, explains, “A system decision is never just technical — it’s financial access,” which is why even a single automated action can have immediate real-world consequences for workers relying on timely wages.

That reality has made a human-in-the-loop (HITL) approach foundational to scale, compliance and trust.

Accountability is replacing automation as the benchmark

As AI systems take on more decision-making, the benchmark for success is also shifting to greater accountability. Regulators are not just asking whether a fraud tool exists, but who is responsible when it makes a mistake.

This focus is reflected in evolving frameworks like the GENIUS Act and stricter expectations from PCI SSC, where traceability and clear intervention pathways are becoming mandatory.

In a paycard ecosystem, which mainly serves the underbanked and gig workers, a false positive that freezes a payroll card isn’t considered a technical issue. Manju says, “If an automated decision is not clearly owned, it disrupts someone’s livelihood and introduces risk rather than reducing it.”

HITL models ensure that decisions remain reviewable, reversible and tied to human accountability.

Scale requires judgment, not just speed

At the same time, the need for scale has not diminished. That’s where AI excels, capable of processing massive volumes and reviewing thousands of transactions and KYC checks in seconds. But speed alone does not guarantee accuracy.

What AI lacks is the ability to recognize nuance. Many flagged anomalies are not fraudulent but are based on patterns outside training data — seasonal spending, worker-specific habits or localized trends, as examples.

Manju points out, “AI can identify patterns, but it doesn’t understand context. That gap is where most risk decisions are made, which is why relying solely on automation can actually lead to unnecessary friction for users.”

Explainability has become operational

This balance is also driving a shift in how systems are designed. The era of opaque, “black-box” decisioning isn’t viable anymore in a regulated environment. Paycard providers are moving toward more transparent, “glass-box” models for real-time visibility.

Every action must surface its rationale, and low-confidence outputs must trigger escalation automatically, according to Manju. She says, “If a decision can’t be explained at the moment it’s made, it can’t be defended later. Explainability is now an operational requirement rather than a technical enhancement.”

Managing risk is a new differentiator

AI has already revolutionized the industry and with the emergence of agentic AI, how a paycard provider manages risk within automated systems will be a new differentiator. Leading organizations are placing greater emphasis on clear escalation paths, consistent handling of edge cases and visible human intervention.

“Automation improves efficiency. Confidence comes from knowing where human judgment steps in, which is ultimately what builds trust with both clients and end users,” says Manju.

The autonomy paradox

The move from simple automation (following rules) to AI (predicting outcomes) and finally to agentic AI (independently acting on predictions) necessitates greater human-in-the-loop accountability.

HITL oversight ensures that decisions hold under scrutiny and remain aligned with real world impact. Manju emphasizes the responsibility that comes with operating in this space.

She says, “We’re not just processing transactions, we’re safeguarding access to earnings.”

The paradox of agentic AI is that it removes the routine to make HITL oversight indispensable.