In August, UK regulators hit Monzo with a £21 million fine. The reason? 34,000 risky accounts slipped through onboarding controls.

The regulator’s view was simple: the “front door” wasn’t strong enough. Inadequate checks at onboarding allowed high-risk customers to open accounts, leaving the bank exposed to financial crime. The cost of that oversight? Eight figures.

But here’s the uncomfortable truth: the failure wasn’t because Monzo didn’t have controls. It’s that the controls in place couldn’t keep up. Traditional compliance systems are rigid, slow to adapt, and too dependent on manual review. That’s where modern AI-driven approaches make the difference.

The Missed Opportunity

Monzo’s onboarding process did what most banks’ do: collect KYC data, run it through standard screening, and apply some risk rules. On paper, it sounds reasonable.

But in practice:

1. Geolocation mismatches weren’t flagged (e.g. a UK address paired with an IP in a high-risk jurisdiction).
2. Implausible customer data slipped through (unrealistic income/job/age combinations).
3. High-risk flows weren’t stopped at the gate, meaning suspicious accounts went live before a human could intervene.

The result: tens of thousands of accounts that regulators said should never have made it through.

The 4-Minute Fix: An AI Agent for Onboarding

With spektr’s Agent Builder, you can design an onboarding agent in minutes that does what legacy systems can’t: catch anomalies in real time and adapt as risks evolve.

Here’s what it looks like:

Address Verification & Plausibility Screening

• Research, evaluate, and verify addresses.

• Run plausibility checks (e.g. income vs. occupation, age vs. employment type).

Proactive Anomaly Detection

• Spot mismatched or inconsistent onboarding data.
• Flag unusual patterns (e.g. many signups from the same device, or identical documentation across multiple accounts).

Automatic High-Risk Blocking

• Instead of letting accounts slip through, block suspicious flows until a compliance officer reviews and approves.
• Every block and review is logged automatically for auditability.

The build time? About four minutes. The outcome? A dynamic first line of defense that reduces manual workload while catching risks in real time.

The Bigger Picture

The £21m fine isn’t just a penalty for Monzo. It’s a signal to the entire industry: regulators expect proactive, adaptive onboarding controls. Relying on static, rules-based systems isn’t enough. And the cost isn’t just financial. When compliance controls fail:

• Risky accounts are harder (and more expensive) to unwind later.
• Regulatory credibility takes a hit.
• Public trust erodes.

AI agents aren’t a silver bullet. But they supercharge compliance teams by closing the gap between manual oversight and the speed of modern financial crime.

What’s Next

This is the first in our series where we’ll show how AI agents could have prevented recent compliance failures in under four minutes each.

Next week: Barclays and the £42m escalation miss.