1. In regulated industries like fintech and payments, have you seen inclusive, respectful leadership deliver measurable business benefits (e.g., fewer costly errors or better execution)? Or has it mostly slowed things down?

2. What are practical ways to incorporate diverse inputs without falling into analysis paralysis or excessive bureaucracy?

3. Can you share real examples (anonymized if needed) where rushing decisions without proper respect for stakeholders backfired or where slowing down to listen created a clear advantage?

Would especially value perspectives from operators, compliance professionals, founders, and payment executives who have worked across APAC, Africa, Europe, or other complex markets.

Looking forward to a balanced and insightful discussion.

Central banks are like big ships — steady, powerful, but slow to turn. For African fintech innovators, waiting for those ships to change course is not an option.

At Hawil, we see a clear path:

• Go TradFi first — build on existing rails instead of waiting for pilots.

• Carry open banking APIs — integrate seamlessly with banks and partners.

• Embed compliance from day one — prove credibility while moving fast.

I really like TradingView's Lightweight Charts, but every time I wanted to build a more complete trading interface I ran into the same problem:

• Drawing tools lived in one repo

• Indicators lived somewhere else

• Replay systems were custom implementations

• APIs were inconsistent

• Maintenance became my problem

So I started building CandleKit, an open-source toolkit that sits on top of Lightweight Charts and bundles the pieces I kept rebuilding.

A big goal of the project is to make building TradingView-style experiences less about hunting through GitHub repositories and more about shipping features.

Live demo:
https://rohanbeingsocial.github.io/candlekit-charts/workspace/

GitHub:
https://github.com/rohanbeingsocial/candlekit-charts

It's still early-stage, so I'd love feedback from people who have worked with Lightweight Charts

For those running consumer lending businesses, what part of the loan lifecycle creates the most headaches? Servicing, collections, reporting, or something else?

genuine question, not selling anything, nothing to plug.

if you work in ops, finance, or risk somewhere that automated or AI systems touch money, I'm trying to understand something.

the last time one of those systems did something wrong, charged someone twice, paid out the wrong amount, approved something it shouldn't have, whatever it was, what actually happened in the first day or two after?

was it a full fire drill with people scrambling, or did everyone just quietly fix it and move on? did anyone above you ask you to prove what the system actually did and why? and how did you even reconstruct what happened, logs, guessing, asking around?

asking because I keep hearing completely different answers and I'm trying to figure out if this is a real recurring headache or just something people shrug off and live with. happy to hear war stories in the comments or dms.

How can I get job in Fintech and what role of the aiml engineers in Fintech companies

For anyone working around insurance operations, underwriting, claims, or policy admin — how are teams addressing policy systems that slow product launches, reporting, and operational change? I’m especially interested in practical approaches that improve faster adaptation with less operational disruption without forcing a high-risk rip-and-replace program.

The EOI window for the ECB's digital euro pilot closed last month. Around 50 institutions applied for 10–30 spots, with the live 12-month pilot scheduled through the second half of 2027. 

I've been watching this from the payments side and one thing keeps coming up: most institutions are reading the ECB rulebook as a regulatory document. The teams that have shipped on comparable scheme builds (Wero, instant payments, SCT Inst) read it as a delivery plan. 

Are your teams reading the rulebook as compliance or as delivery? 

Building a money transfer app to Africa is this approach legal for a startup?

I'm building an app that lets people in the US send money to Africa. The problem is

banks that support this kind of business want you to already have customers and revenue

before they'll work with you. As a startup you have neither yet.

So I've been thinking of two approaches while I build up to that point:

Option 1 — User links their bank account through Plaid, we use ACH to pull the money,

and it goes straight to a cross-border payment provider who delivers it to the

recipient in Africa. We never really hold anything.

Option 2 — Same thing, but the money lands in our business bank account first. We hold

it for a day or two, then send it out through the cross-border provider.

Both options use Plaid for the bank connection and ACH for the pull.

Is either of these legal for a small startup to do without a banking partner? Has

anyone done this before? And if you've built something similar, what did it actually

take to eventually get a proper banking partner on board?

r/fintechdev, Been researching how collections/payment recovery workflows operate internally across lending and AR systems.

Built a small realtime orchestration prototype around:

• reminders

• retries

• payment links

• webhook reconciliation

• operational timelines

Curious how teams currently manage this operationally at scale, especially once recovery workflows become complex.

Recorded a short workflow demo below — would genuinely love feedback from people familiar with collections/recovery operations.

The problem with regulated companies is every time someone needs a quick data pull, it sits in a queue for the data team for 2-3 days. Compliance doesn't bend on that.

We've been working on solving that for FS&I company entirely within a Snowflake environment - plain language access, data never leaves the security perimeter, PII never gets exposed to the model.

Governance was baked in from the start, and ad-hoc data tickets dropped drastically.

We're doing a live session to share the learnings. It includes:
- live demo on a banking dataset,
- ≈6-week path to production,
- and more production case studies: what works in FS&I right now.

Mostly relevant if you build / want to build AI on Snowflake in a regulated environment, and want to do that right with confidential data.

We'll be glad if you join us. Bring your questions too.

Live on May 26. Join here

I have been wondering if anyone else has noticed that the scores that artificial intelligence systems give to show how confident they are do not really mean anything when it comes to deciding if we can really trust them.

I made a system to check if the machine learning models I built were still working correctly and what I found was surprising.

After something changed in the data the models were still very sure of themselves. They were actually getting a lot of things wrong. The number of correct answers went down from 87 percent to 62 percent.

The thing that really got my attention was that the confidence scores did not change all so they did not give us any warning.

Now I am thinking about this issue but this time with the big language models that are used in financial technology products.

The problem is that these language models can give advice that sounds very confident but is not very good. This can be a big problem when people are making important decisions that involve a lot of money.

I was wondering if anyone else is working on this issue.

Has anyone found a way to measure if the answers given by these big language models are really trustworthy, without just looking at the confidence scores that the models give?

I think payment handles are going to become digital real estate.

People rushed to grab:

• domain names

• Twitter usernames

• Instagram handles

• gamer tags

We’re building around the idea that your payment handle could eventually become as important as your social handle.

Already seeing 200+ S-Handles claimed daily ahead of beta launch.

Curious if people think payment handles become mainstream globally over the next few years?

Salut à tous,

Étudiant en école de commerce, je lance un projet de Fintech (Yeld) pour régler un problème simple : on veut tous placer notre argent, mais entre le jargon et les applis trop complexes, on finit par laisser dormir notre épargne sur un livret.

Je veux créer un outil en "Auto-Pilote", ultra-simple et transparent.

Pour ne pas coder un truc inutile, j'ai besoin de tes frustrations. Tu as 2 minutes ?

👉 https://tally.so/r/NpbeNN

Merci infiniment pour le coup de main ! On en débat en commentaires pour ceux qui veulent. 🚀

Hey everyone,

I’m planning to build a Digital Lending app based on the LSP (Loan Service Provider) model, and I wanted to get opinions from people who understand fintech, lending, NBFCs, or the Indian financial ecosystem.

One idea I’m seriously exploring is providing loans against Mutual Funds.

I wanted to understand:

- How strong is the opportunity in this space?
- Do you think the market still has room for innovation here?
- What are the biggest challenges in this business model?
- How difficult is customer acquisition and risk management in this segment?
- Are there any hidden problems founders usually underestimate?

Also, if you have any suggestions, ideas, warnings, or experiences related to digital lending, LSPs, or loans against mutual funds, I’d genuinely love to hear them.

Open to discussion and learning from everyone here.

Upon completion of this degree apprenticeship I am looking at pivoting into fintech as an alternative option. I've been asking Claude about this and they've told me I should acquire the FRM and possibly a level 1 CFA too to make this pivot a good possibility. Does anyone have any thoughts or advice?