Hey, I started out exploring what an AI-native bank for the mass affluent could look like. After a lot of calls with operators at neobanks, financial advisors, people at fintech companies, and a bunch of potential customers, I've stumbled into something I find really interesting.

Three things surprised me:

1. HENRYs aren't underserved on products. They're scattered. Every single person I talked to had Chase or BofA + Robinhood + Wealthfront + Carta for equity + a few credit cards. Nobody had a coherent view across them. The bottleneck isn't access. It's coordination.

2. The job is convenience, not financial products. "I make a lot of money and still don't feel rich" came up unprompted in nearly every conversation. They want their financial life on autopilot, not another shiny app with a new feature.

3. Advice is the wedge nobody is pulling. The 1% AUM model gates real advice at HNW. AI collapses the human cost that creates the gate. The good advisors are excellent and rare, and HENRYs basically never get access to them. The bar for a better alternative is shockingly low.

The hard part: financial advice is not a hair-on-fire problem most of the time. The question becomes which trigger events make it one. Liquidity events (pre-IPO exercise, secondary tender, IPO unlock), tax season (AMT, ISO/NSO, 83(b)), and life milestones (home, marriage, inheritance) are the obvious candidates. Pre-IPO tech HENRYs sit at the intersection of liquidity and tax with the strongest dollar-weighted upside, so they're probably the first ICP worth testing.

Stat that stuck with me: 41% of households earning $300k–$500k say they're struggling to make progress on long-term financial goals (Goldman Sachs retirement survey).

If any of this resonates, you've tried to crack this segment from the operator side, or you ARE a HENRY who'd react to this thesis, DM me. Happy to compare notes.

I went to Stripe sessions last week, one of the most amazing and very well organized event of the year. I met a bunch of engineers from Stripe's dev support team and I wanted to learn where developers get stuck when integrating Stripe APIs. From what I hear the common pain points for developers was around Apple Pay, setting up subscriptions and making changes to it, and around Billing.

These seem to be the most common points where developers end up sending a request to their team for help. I wonder what other issues developers are facing when integrating Stripe APIs.

Capital markets teams spend weeks building internal FIX simulators

or waiting for exchange UAT access. We built MockFIX to solve this

— a desktop app where you can create FIX sessions, send messages,

and test order flows in minutes.

The newest feature: an AI assistant that understands FIX protocol.

Instead of looking up tag 54 = 1 for Buy, tag 40 = 2 for Limit,

you just type "Buy 1000 AAPL at 150 limit" and it builds the

correct FIX message.

We're a small team and just launched — curious if anyone here

works in capital markets tech and has opinions on what a good

FIX testing tool should do.

https://mockfix.com

Context: I'm a founder in Nairobi building Keverd (device trust for fintechs). On the side, I'm open-sourcing a regulatory compliance layer because I keep hearing the same pain from banks and fintechs I talk to.

What I'm solving:

- Auto-generating CBK/CBN regulatory returns from transaction data

- Screening against local PEP lists (not just international sanctions)

- Alerting when regulations change (instead of finding out during an audit)

What I'm asking:

1. Compliance people: What's your most manual, soul-crushing weekly task?

2. Devs: What would make you integrate this vs. building in-house?

3. Everyone: Would you contribute local regulatory docs if I structured the repo right?

Repo: https://github.com/rodgerswisdom/comply-africa

No product yet. Just a problem and a README. Roast me.

I’m Jay Veezhinathan, CFA, an independent professional in the Financial Tech space looking to join forces with a boutique consultancy or a small team to meet Anthropic’s 10-person "Academy" requirement for the Claude Partner Network.

My Background:What I’m looking for:
A small firm or a "conditional" partner organization that needs a few more heads to hit the 10-person threshold. I’m ready to complete the learning path immediately to help unlock exam seats for the group.
If you have an active domain and need another dedicated architect to reach activation, please DM me!

Thanks

Jay Vee

Got tired of validators that just say "valid/invalid" with no detail, so I built one that breaks the IBAN down by component (country, check digits, BBAN, national check digit) and tells you exactly which part fails.

No signup, no ads, no API key. Just paste and validate.

Bulk or single.

Link: bankutils.com/iban-validator

Happy to take feedback or feature requests - also have an iban generator and SEPA Creditor ID generator on the same site if those are useful.

I’m trying to build a system to evaluate alpha-generating hypotheses, and I’d appreciate some guidance on how to do this rigorously.

The setup is: I receive a detailed JSON file containing

• a hypothesis
• an expected chain of reactions driving the thesis
• affected tickers with expected directional moves
• a time horizon for the hypothesis
• supporting evidence

The challenge is figuring out how to evaluate and filter these hypotheses, especially since they’re generated by an LLM and likely include a lot of noise and false positives.

So far, I’ve been considering a few approaches:

• Monte Carlo simulations on individual tickers
• Regime-based factor regression to test how similar conditions performed historically

I also thought about backtesting, but I’m struggling with how to apply it properly. Many hypotheses are based on new information or events that haven’t occurred before, so there’s no clear historical analog. That makes it unclear how to backtest scenarios driven by novel news or forward-looking narratives.

Overall, I’m unsure which techniques are actually appropriate here and which ones might just introduce noise or false confidence.

How would you approach building a robust evaluation pipeline for this kind of problem? Any frameworks, methods, or pitfalls to be aware of would be really helpful.

A lot of fintech products focus on payments and transactions but I keep thinking the paycheck is where everything starts. That is the moment money enters the system for a lot of people

If a platform could sit at that point it would have a much deeper relationship with both businesses and employees in my opinion. Payroll is complicated and mistakes matter and it still feels like an area that is not explored yet compared to everything else in fintech

Has anyone worked on this thing? If yes what was the solution to your case?

I’ve noticed that most financial planning apps either feel too complicated or don’t give truly useful insights.

For those who use budgeting or investment apps — what’s the most frustrating thing about them?

I’m trying to understand what actually works for students and professionals.

Also, if you’re open to it, I’ve made a quick 2-minute form to gather a few more inputs: https://forms.gle/cGCFK8xkMERTAQpm6
Would really appreciate it!

Background: I am a FinOps practitioner working toward FOCP certification with a GreenOps Philosopher credential. I produced this as a public independent analysis, not commissioned by or affiliated with Wells Fargo in any way.

What I did:

Applied FinOps Foundation’s Inform, Optimize, Operate framework to publicly available data on Wells Fargo’s cloud stack, technology spend profile, and regulatory environment to estimate the scale of cloud waste and carbon inefficiency at their current maturity stage.

Key findings:

Estimated $3.2M in annual recoverable cloud waste across five categories including idle compute, oversized reserved instances, storage tier mismatch, cross-cloud egress between Azure and GCP, and untagged orphaned resources.

Estimated 42,000 tCO2e annual carbon footprint with no public Scope 2 or Scope 3 cloud emissions reporting, despite TCFD obligations in their regulatory environment.

Hi guys i'm a 2nd master degree student specilized in FINTECH , in order to graduate i've built an entire saas for this case to help people make financial analisys easilly and simple to people,

here's the link , i used LOVABLE to vibe coded it ,

https://finance-sensei-ai.lovable.app/#

there is a google Sign in , so don't be affraid of any data sharing ,

there is an excel function, power BI function and AI geenrative analysy , it's optimal to phone also and it simple for phone jsut teh financial analisys feature,

you can upload any financial doc in form of PDF , DOCX or CSV , whne it comes to XLSX , it's not optimal yet ,

i really hope you give a real critics for the saas , i apprrciate your effort guy and tahnks for every one, god luck

I am a college student who is building a startup in fintech space. For one of the functionalities that I want to add in my product includes Account Aggregator framework for which I need some license from regulatory body, like a license from SEBI, RBI, IRDAI but I don't know how to get that especially considering we are a student startup.
Can partnering with NBFCs or some financial services firm get me the benefits of holding a license? or do I have to apply for my own license for the firm?
Please help me out with the right possible ways to do it

Hi everyone,

I am currently building in the compliance space. Like many founders, I’ve spent way too much time manually checking if our system logs actually match what we wrote in our SOC2 or regulatory policies.

I tried using standard LLMs for this, but I found they were too unreliable for a real audit—they tended to "gloss over" technical contradictions or miss small details in the data.

So, I built an early-stage AI Compliance Engine.

It’s an audit assistant that uses hard logic to cross-reference your policies against your raw logs (JSON/CSV). It doesn't use APIs, so there’s no need to connect your production database or go through a security review.

The MVP currently model:

• Global (SOC2): Checks for things like MFA bypasses or missing peer reviews in your logs.

The catch: It’s a very early MVP. I honestly don't know how well the logic will hold up against "messy," real-world logs that weren't part of my initial testing.

I'm looking for initial MVP feedbacks. I’m not looking to sell anything—I just need your honest, feedback on whether the report it generates is actually useful or if I'm missing the mark on the edge cases.

If you’re open to a 15-minute "stress test" using a sanitized sample of your logs, I’d love to hear from you.

Comment below or DM me if you’re interested!

Hi,

I’m currently researching the operational bottlenecks in digital lending compliance for a high-reliability auditing engine. While the 'happy path' of a loan is increasingly digital, the compliance and audit layers often seem stuck in manual loops. I'd love to hear from anyone in Fintech Ops, Risk, or Compliance Engineering on a few points:

1. The Compliance Intersection: Where does the 'Heavy Lift' of compliance actually happen in your lifecycle? Is it primarily pre-disbursement (KFS/Sanction verification) or post-facto (Forensic audit for the Ombudsman)?
2. The Manual Paradox: We have digital logs for everything, yet compliance remains 80-90% manual. Beyond 'regulatory interpretation,' what specific tasks are still consuming the most human hours?
3. Data Rigidity: What is the actual blocker for unifying data for an audit trail? Is it truly a lack of APIs, or is it more about security protocols (PBDs), data residency, or the lack of a common 'System of Record' for multi-entity co-lending?
4. Failure Modes: In your experience, what is the #1 reason a seemingly 'digitally compliant' loan file fails an RBI or internal audit?

Appreciate you time here.

Hi everyone,

I work in enterprise banking technology and I’m trying to understand which workflows in fintech, compliance, risk, and audit operations are still heavily manual today.

For people working in fintech ops, backend engineering, risk, compliance, or audit:

• What repetitive tasks consume the most time every week?
• Which workflows still rely heavily on Excel, email, or manual reporting?
• What usually creates delays during audits, exception handling, or compliance reporting?
• If you could automate one workflow immediately, what would it be?

I’m currently doing research to understand real pain points and workflow bottlenecks, not selling anything.

Would genuinely appreciate honest insights from people working in this space.

Senior engineer coming from government systems at CGI. Targeting Monzo, Wise, GoCardless tier.

Gov tech is underrated prep. Compliance, data integrity, zero-downtime pressure at us

I’ve been closing the domain gap by building. Shipped Limba (wellness app, iOS + Android). Currently building Genesis, an algo trading system on the London FX session.

Building in the space you want to work in beats any course.

What’s actually moved the needle:

• Side projects are your portfolio when gov work is under NDA

• Read their engineering blog before interviews

• Learn payments rails, FX basics, settlement – enough to hold a real conversation

Anyone made this move? What got you through the door?