Retail banks

More of everyday life is paid digitally, so more transactions go through your bank than ever. Money stolen from your customers through scams is often laundered through other accounts at your bank.

Criminals use new tools, including AI, to make what they do harder to spot. Rules do not learn from that, so they catch these cases less often. And your team cannot see what the rules are missing.

The alerts keep growing, but your financial crime team cannot grow with them. Hiring more people is expensive, and experienced financial crime specialists are hard to find.

And when most of the day goes to false alarms, it gets harder to keep good people on board.

AMLA and new regulation bring stricter rules and more thorough checks. It is no longer enough to show that controls are in place: regulators look at whether those controls actually do what they are intended to do.

And can you explain, for every alert, why it was followed up or closed?

If regulators find gaps, the consequences can be serious: heavy fines, legal action and damage to your reputation that lasts for years.

Your solution learns from the patterns it was given. But criminals change their methods, and over time its results drift away from what it was built to catch. You often only notice when something has already slipped through.

Can you keep it accurate every day, and retrain it several times a year?

When a regulator comes in, your team has to explain how your solution reaches its results. Why was the AI retrained? Does it still catch what it caught six months ago?

Those answers have to hold up, months or even years later.

Every time the AI runs, it costs money. In a large bank, those costs are spread across many teams and invoices, often without a clear view of what each one delivers.

Have you calculated what it really costs to keep your own solution running?