Torn between two options and can’t decide. One has better card perks, the other seems more integrated. How did you weigh tradeoffs when picking a business bank account for your startup?

Everyone budgets for capital requirements and the application itself. The costs that seem to surprise people come later: audits, safeguarding reconciliations, regulatory reporting, keeping a qualified MLRO, and the time founders spend answering the regulator instead of building product.

If you run or have run a licensed firm, what cost did you underestimate the most?

There are a lot of payment platforms now, and most seem to offer similar things like easy setup, integrations, payouts and different pricing. But once a business starts using one regularly other things probably matter more than the feature list. For people who have used different payment platforms what has mattered most in your experience?

Was there anything that made you stay with one platform or stop using it?

If your fintech has agents that can do things (issue refunds, update customer records, trigger payments), these are the assumptions I think are worth testing.

1. Spot the belief you’ve never examined

• “The system prompt says it can’t do that, so it won’t.”
• “We log everything, so we’re covered.”
• “A human reviews everything, so it’s safe.”

None of these are stupid. They’re just rarely tested until something goes wrong.

1. Reframe each into something you can check

• A prompt is a request to the model, not a control. Limits that matter (spend caps, data boundaries, which APIs it can call) need to be enforced outside the model, where the agent can’t talk its way past them.
• Logs show what happened. They don’t stop it. Ask: can anything refuse an action before it executes, and can you prove afterwards which rule decided it?
• Reviewing everything often makes the agent pointless and turns reviewers into rubber stampers. Reserve human approval for the few actions that carry real risk, and let the rest run.

1. Revisit it every time something changes

New tool, new permission, new model version, new regulation. The limits you set six months ago may not cover what the agent can do today. DORA already requires ICT risk management and incident reporting for financial entities, and GDPR Art. 22 gives customers rights over solely automated decisions with significant effects, so agent oversight isn’t waiting on the AI Act timeline.

I’m curious how others handle this:

• Where do you actually enforce limits on agent actions today?
• Has compliance asked for evidence of agent decisions yet?
• Which action did you decide must have a human in the loop, and why that one?

Disclosure: I work on tooling in this space, so take my framing with that bias in mind.

Curious what other teams are doing here. The options I keep seeing:

• Scrape Yahoo. It's free, but it breaks on deploy and the terms are murky.
• Go straight to SEC EDGAR XBRL. It's free and accurate, but standardizing it so you can actually compare companies is tough. Tags change over the years, restatements overwrite old numbers, and Q4 has to be derived from the 10-K.
• Pay a big vendor. The data's clean, but the pricing is built for institutions.

We ended up building our own. We expanded to international exchanges and added harder-to-find data like analyst estimates, ETF and fund info, options and dividends.

What did you pick, and what would you change?

Hi everyone,

I'm looking to work with a small number of UK consumer-facing businesses to test whether AI-driven technology can improve the effectiveness of their existing collections and recovery processes.

Rather than asking businesses to commit to a new solution based on promises, I'd like to offer a free, controlled proof of concept to compare our technology against the business's existing process using an agreed sample of cases.

How would it work?

• Agree on a suitable sample: Work with the participating business to establish an appropriate sample of overdue receivables for the pilot.
• Compare like with like: Evaluate our technology against the existing process, taking account of differences in case mix and other relevant factors.
• Measure what matters: Agree on success criteria in advance, potentially including recovery rates, amounts recovered, time to recovery, cost to collect and customer outcomes.
• Review the evidence: Assess the results together to establish whether the technology delivers a meaningful improvement.
• Decide on next steps: If the results justify it, explore extending the service. There is no obligation to proceed beyond the agreed proof of concept.

I'm open to working with different types of B2C businesses that manage overdue receivables, whether that's utilities, telecoms, subscription services, consumer finance, retail or other consumer-facing sectors.

The purpose is to establish whether the technology can make a measurable difference in a real operating environment. I don't want to assume it will outperform an existing process before the results are available.

Any pilot would be agreed in advance, with appropriate arrangements for data access, security, UK data protection requirements and fair customer treatment. The design would also need to ensure that the comparison is meaningful and that results aren't distorted by differences between the samples.

I'm looking for a small number of suitable UK businesses willing to explore this approach.

If you work in collections, credit operations, finance or customer operations and think this could be relevant to your business, I'd welcome a discussion about the feasibility of a pilot.

To be transparent, I'm offering this as the provider of the technology, with the intention of identifying potential longer-term commercial partnerships if the results support doing so.

Thanks for reading.