To Fight Financial Crime, AI Needs Context

Financial crime remains one of the costliest and most persistent challenges facing banks and financial institutions worldwide.

The UN estimates that criminals launder between US$800bn and US$2tn annually.

At the high end, that is roughly equivalent to the annual GDP of Italy or Canada. Yet despite spending more than US$200bn a year on compliance, financial institutions intercept only a small share of illicit funds, often less than 2%, according to the UN.

At the same time, banks, fintechs and other financial institutions are investing heavily in AI to strengthen fraud detection, improve compliance and drive greater operational efficiency.

But many will struggle to achieve meaningful returns because of a deeper problem: poor underlying data.

Fragmented, inconsistent records spread across customer, account, transaction and counterparty systems limit what AI can actually do.

AI can process signals faster, but it cannot resolve disconnected data or supply missing business context on its own.

That is why institutions need a context intelligence system, one that unifies and connects enterprise data so AI can operate with the trusted, real-time context required to detect risk faster, make better decisions and deliver stronger business outcomes.

The cost of fragmented data

Financial institutions are racing to deploy AI to improve fraud detection, streamline investigations and reduce compliance burdens.

But AI cannot repair fragmented data or infer the business context missing from disconnected systems. If institutions feed AI incomplete, inconsistent or duplicated records, they will get faster but deeply flawed outcomes.

That is why context matters. Before AI can deliver meaningful value, institutions need a way to unify customer, account, transaction and counterparty data into a trusted, connected view of the business.

"AI on its own is like hiring a brilliant employee with no knowledge of your business," says Manish Sood, Founder and CEO of Reltio, a context intelligence company.

"To make it useful, you have to give it context, trusted information about your customers, products and operations.

"Without that, it can’t make decisions that move the business forward."

Agentic AI raises the stakes

That challenge becomes even more urgent as financial institutions move beyond basic automation and begin experimenting with agentic AI.

In theory, the appeal is obvious. Who would not want a squad of AI agents able to independently execute end-to-end fraud investigations and deliver significant productivity gains, as McKinsey has estimated?

Yet the potential upside depends entirely on the integrity of the underlying data and the quality of the context surrounding it.

In other words, agentic AI does not solve the data problem. It magnifies it.

Without unifying fragmented data to create a trusted foundation, financial institutions will struggle to realise AI’s promise and may end up compounding errors at scale.

From defensive compliance to proactive risk management

A trusted context intelligence foundation does more than eliminate regulatory pain points.

It enables institutions to shift from reactive compliance to proactive risk management while improving speed, decision-making and operational efficiency.

For example:

•  Real-time customer profiles enable continuous monitoring instead of periodic reviews
•  Enriched transaction histories make it easier to identify emerging fraud patterns
•  Unified views of corporate hierarchies support sharper counterparty risk assessments.

By operationalising trusted data across KYC, AML and fraud use cases, financial institutions can enhance resilience, reduce costs, improve productivity and deliver better experiences, all while staying ahead of regulators.

The path forward

The institutions that invest in real-time, governed, connected data will be best positioned to turn compliance from a defensive necessity into a strategic advantage, mitigating risk, satisfying regulators, improving the return on AI investments and earning customer trust in a digital age where data integrity defines success.

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