A host of banking giants, including Barclays and Deutsche Bank, are already using a new Ant International AI model to improve their cashflow forecasting and FX liquidity management capabilities.
A host of banking giants, including Barclays and Deutsche Bank, are already using a new Ant International AI model to improve their cashflow forecasting and FX liquidity management capabilities.
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Why it matters
Financial services has one characteristic that makes it unlike every other sector: it can fail on experience and survive, but it cannot fail on trust. That is why moves that look slow from the outside are usually prudent from the inside.
I separate what improves the customer's life from what improves the bank's margin. Sometimes they coincide, and those are the good projects. When they do not, it is worth saying so out loud in committee, because a project that only improves margin shows up in customer satisfaction sooner than people expect.
What usually goes wrong
What I see fail most is underestimated compliance load. The product gets designed around the customer and legal comes in late, once it is already built. That is when requirements appear that force whole flows to be redone, and what was going to ship in one quarter ships in three.
What to watch
- What happens to cost per transaction at real volume, which is where the business is decided.
- How daily reconciliation is left, which is where the cases nobody modelled show up.
- What the local regulator requires and by when, because the deadline is usually the real constraint.
How I read this entry
What I would ask for before approving something like this is the plan for when it fails: how the customer gets their money back, in what timeframe, who authorises it and what they are told meanwhile. In payments, the quality of the recovery matters more than the quality of the happy path.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
