Banks Are Losing Customers at ‘Hello’

Banks sell trust. Yet their first handshake, the onboarding process, feels like an interrogation.

Lengthy forms. Repeated uploads. Endless verification loops. What should welcome customers instead tests their patience and their loyalty. Behind the curtain, compliance teams are overwhelmed by Know Your Customer (KYC) and Anti-Money Laundering (AML) reviews. They chase false positives, patch fragmented data, and fight the clock to stay “compliant.”

And it’s costing banks both money and trust.

Global AML compliance costs are projected to reach $51.7 billion by 2028. The average bank already burns $64 million a year chasing red flags that often aren’t real. Onboarding 10,000 clients? That can run $35 million annually, with a quarter of applications abandoned before completion.

Then in 2024, the $3 billion fine on TD Bank for AML lapses was a warning siren for the entire industry. Compliance failures are not just reputational risks; they’re existential threats to institutions.

The message is simple: banks can’t keep scaling with systems designed for paper, not pattern recognition.

Rules Don’t Think. Agents Do.

Traditional automation gave banks efficiency. It didn’t give them adaptive intelligence.

When a customer’s document doesn’t fit a template or a transaction looks “off,” legacy systems freeze. Humans jump in. The loop restarts. Agentic AI breaks the loop.

Where traditional systems wait for rules to trigger, AI agents observe, learn, and act. They detect patterns. They reason through anomalies and self-correct. A KYC agent sees a legitimate different regional ID in a slightly different format and recognizes it as valid, with no false rejection. A risk agent learns from past reviews and reduces false positives before analysts ever touch the case. The result isn’t just faster processing. It’s fewer dead ends.

HSBC reduced false positives by 60% using AI-driven compliance monitoring. JPMorgan Chase introduced generative AI in its client onboarding process and significantly slashed validation and onboarding time.

Agentic AI isn’t just automation. It is an augmentation where machines think in context and not through checklists.

Meet the New Analyst: AI with a Brain

Agentic AI goes beyond digitizing tasks, reshaping the architecture of onboarding.

• Intelligent Document Processing:Agents pull, read, and verify data from IDs, financial statements, and supporting docs –cutting manual data entry by up to 60% while ensuring instant compliance checks.
• Dynamic Risk Assessment:Risk profile is updated in real time as agents integrate data from transactions, watchlists, and third-party systems. Banks can process three times more applications without compromising compliance standards.
• Autonomous Decision-Making:For low-risk customers, autonomous onboarding happens in minutes, not hours, while maintaining full audit trails.
• Continuous Compliance Monitoring:AI agents watch every transaction in motion, flagging suspicious behavior before it escalates.
• Intelligent Exception Handling:When anomalies arise, agents explain why. Humans step in where judgment, not repetition, matters.

Customers move from “waiting to bank” to “ready to transact.” That’s not compliance optimization. That’s experience engineering.

Start Small. Think Big. Move Fast

The journey to agentic onboarding doesn’t have to start with a massive overhaul. It starts with precision, evolving the systems, adding one intelligent layer at a time.

Start by adding an intelligent overlay. Don’t tear down your entire KYC infrastructure. Embed AI agents on top to validate documents and check sanction lists, and catch inconsistencies before a human steps in.

As confidence builds, design microservices for critical areas like autonomous risk-scoring, or document-verification modules that operate independently but sync seamlessly with the core banking architecture.

The ultimate goal is afully agentic ecosystem. Purpose-built AI agents that talk, critique, and collaborate in real time. Onboarding becomes an adaptive network, not a static workflow.

But autonomy without accountability is chaos. Regulatory logic must live inside the model’s DNA. Auditability. Explainability. Traceability. Every decision should be as transparent as it is fast. And the human role changes from processor to orchestrator. From compliance clerks to AI supervisors, humans still hold the steering wheel. They drive a more intelligent machine.

The Real Risk

The problem isn’t that banks can’t meet compliance standards. It’s that they’re paying a premium for inefficiency.

In most industries, automation failure costs efficiency. Whereas in banking, it costs trust, capital, and credibility.

Agentic AI has shifted from being a technology choice to being a survival necessity. It transforms onboarding from a regulatory burden into a strategic capability, one that moves as fast as your market and adapts as precisely as your risk appetite.

The next generation of banking won’t be defined by who complies. It’ll be defined by who learns faster.