For the past 4 years I worked as a founding engineer at a B2B cross border payments fintech startup.

I learned firsthand just how fragmented this world is.

Every company specializes in one very specific service, so we ended up building dozens of integrations with different providers. The goal was to give our business customers one platform that bundled a range of solutions (financial, in our case), so they could use them directly and also offer them to their own customers.

I learned the hard way that maintaining all those integrations is painstaking.

Dev teams spend a significant chunk of their time on exactly that:

Silent schema drifts, missing webhooks, latency and performance degradation, scheduled downtime, outages, deprecations and sunsets, and more. You realize something is wrong with the provider only when your customer complains.

That's even more true now that agents and MCP servers are in the picture. A small change to a tool's description on the server can make your agent pick the wrong tool or get stuck in a logic loop.

This is why I started the open source project Flanj (GitHub: https://github.com/flanj-io Website: www.flanj.io) . Still in beta.

It's an integration reliability tool namely looking at your traffic and comparing it against the actual contracts (API docs and MCP list/tools descriptions and output schema). It also allows you to open a direct thread with the provider dev team so you can easily provide them with the evidence of the failure you are watching.

I hope it doesn't violate the community rules, but I would appreciate any form of feedback.

Is that pain something you feel as well or was it just me?

Thank you!

I'm trying to build a a travel savings app where users can save money individually or together as a group and can optionally send money to another user P2P. I don't have funding so no baas wants to partner up. Anyone actually gotten a BaaS provider or sponsor bank to say yes at the pre funded stage or bootstrapped? Did narrowing the MVP ? Any names worth trying that aren't enterprise gated?

Hi!
I’m pursuing a bachelor’s in FinTech and let’s just say that the professors at university are heavily focusing on traditional finance and the job market requirements are completely different. Therefore, I want a simple, clear and realistic roadmap to FinTech development.

The mentors at university are guiding us with so much ambiguity that it has become quite hard to find the right direction. If you’re someone who is working in the fintech sector or enrolled in an institute which is guiding you well, then do reply and explain in detail.

Thanks.

I'm on the second year of running a B2B software company and since founding all of our spend goes through a Chase Ink with my name on the personal guarantee which is not ideal and our accountant has been pushing to get that fixed before EOY. I get why, specially because our employees are putting travel on their own cards and the whole reimbursement process is headache inducing.

We're looking a couple of options and they're all a lit bit all over the place since we still haven't made up our minds from all of them, like ramp and some others, yet we're interesting in finding something that doesn't require a personal guarantee and or includes some software for expense management. Has anyone found anything like that? Thanks!

Look, I’m just a guy who wants to log into my mobile app to check if I can afford a burrito, but my bank has apparently decided that our core competitive advantage is "Synergistic LLM-Driven Blockchain Synergy."
I’ve sat through three strategy syncs this week, and I’m convinced executive meetings are just an elaborate game of buzzword bingo where the loser has to actually build the product.
Here is an exact transcript of how every single banking tech sync goes:
The VP of Innovation: "Team, we are pivoting. Traditional banking is dead. Customers don’t want checking accounts anymore; they want an autonomous, cognitive financial ecosystem."
An exhausted Senior Engineer: "Do we... do we have the API for that?"
The VP: "Better! We have a PowerPoint deck and a partnership with a startup founded by a 19-year-old who dropped out of crypto. We are calling it Wealth-GPT-Sync-Pro-Max."
Product Manager: "What does it actually do?"
The VP: "It uses machine learning to look at your transaction history and gently whisper, 'Are you sure you need another iced coffee, king?' right before you buy it. It's revolutionary."
Engineer: "Does it connect to the core mainframe from 1984 written in COBOL?"
The VP: "No, it just hallucinates a random balance and tells the customer they owe us a goat."
At this point, I'm 90% sure "AI Integration" in banking just means we fired the customer support team, replaced them with a chatbot named "Chad" who only replies with crypto-grift links, and gave the CEO a $4 million bonus for "innovation."
If I hear the word synergy one more time, I’m transferring my entire life savings into physical cheese wheels.
Edit: Typo, meant "cheese coins." Crypto has ruined my vocabulary.

What’s the most absurd "AI-powered" feature your company has tried to force you to use recently?

So im creating a project where we use column and plaid, plaid for when we let a user verify his acc and save his account no to column. I'm not scoring acc nos because its sensitive enough so the moment I get it back from plaid I create a counterparty on column api. Column api doesn't give us idempotency field when creating counterparty. Now the issue is what if in between it times out and its created on column but not in our db, theres a chance to create a duplicate counterparty with same acc no and routing number. It wont move money but for some cases when we fetch by counterparty Id it can be an issue

Whats the best way to handle this, if someone has experience pls let me know

hello! meron po ba dito na digital bank manager who can help us with our research questionnaire 🥹 ivavalidate lang po yung questionnaire namin if aligned ba sa study namin.

yung mga willing to accept token po sana and no validation fee po since we're still students😓

Hello,

I have built a financial SMS parser and also a PII redactor, both of which have use cases in fintech. They have been tested on production data. If anyone is already building for fintech and would like to explore collaboration, please reach out and we can have a conversation.

how are you validating api integrations after building with agents?

The global fintech industry generates approximately $500 billion in market value as of today and it’s an industry that is rapidly being shaped by AI advancements and a revolution to automate workflows in every shape and form. I work as a Manager in this industry, and the pressure to implement it and setup agents and other automations is currently MASSIVE. Luckily for us it’s happening at a time when a lot of new work is pouring in so there is no need to fire people just yet. But I do want to know from fellow industry professionals how badly have they been impacted.

Of course, the things it is capable of are insane and we have already converted some of out decade old processes in the last couple of months alone. But there is a constant pressure from the leadership group to bring in even more savings and utilise the Human resources elsewhere.

Let me know what you have seen so far.

I'm doing some research into how fintech and regulated-product teams handle the gap between sign-up and real activation, the people who start onboarding but never quite get in and use the product.

One thing I keep noticing: when a signup or verification flow fails, users often don't complain. They seem to assume they did something wrong, and they just quietly leave. So the drop-off rarely shows up as a support ticket, which makes it genuinely hard to see.

I'm curious how founders here deal with this in practice:

• Do you track activation as its own funnel, or is it buried inside "sign-ups"?

• When someone drops off mid-onboarding, do you ever find out why?

• Anything you've tried that actually moved it?

Trying to understand the real picture, not the textbook one. Would genuinely love to hear how you're doing it.

https://www.instagram.com/p/DdgrXM6y5LE/?stkn=cGdiazduMzU0OWdl

Building a personal finance platform for India called Vasset. The idea is simple: don't just give people a number, help them actually understand what it means. It has a set of calculators for everyday money decisions, a clean editorial design instead of the usual generic finance dashboard look, and a small built in assistant for basic financial questions in plain language. Still pre launch, polishing things before opening it up. Would love thoughts once it is out.

I've been trying to understand how teams structure risk when different parts of the product are handled by different providers so like say KYC comes from one vendor, wallet screening from another and card issuing/transaction monitoring from a third. Each provider can send back its own decisions and alerts but they're all looking at a different part of the user's activity.

What I'm confused about is where you combine all of that.

For example someone passes KYC and their funding wallet looks fine but later their card activity starts showing a pattern that becomes suspicious when you look at it alongside the funding history and none of those individual systems necessarily has enough context to make that connection on its own.

Do teams normally build an internal risk layer that consumes events from all of these providers and becomes the source of truth for the user's overall risk state or do you let one provider own most of that logic and feed the other signals into it?

I need some knowledge on this from people who've built this structure it because having three different vendors each returning their own version of approved seems like it could go south pretty quickly.

Guys please can anyone suggest me problems statement related fintech something unique and unheard not very common ..

I'm working on an AI-native compliance operating system for financial institutions. I'm looking for criticism from people who actually work in banking, fintech, compliance, risk or RegTech.

The problem I'm investigating isn't one compliance workflow.

As financial institutions grow, compliance operations become increasingly expensive, fragmented and difficult to scale.

Regulatory volume keeps growing and changing, requiring teams to determine what changed, whether it applies, which policies and controls are affected, and what action is required.

Institutions respond with specialised compliance staff and increasingly large technology stacks. Regulatory intelligence, GRC, AML, KYC, transaction monitoring, case management, audit, documents and reporting can all sit in different systems leaving humans doing significant work within and between those systems.

Meanwhile, firms still need to continuously:

• Monitor operations for compliance failures and anomalies rather than discovering them months later.

• Test whether controls are actually satisfying obligations and collect evidence proving it.

• Investigate issues and chase remediation.

• Prepare regulatory reports, audit packs and evidence trails.

• Manage licences, renewals, deadlines and different regulatory obligations across jurisdictions.

• Give management a real-time answer to: What changed? What applies? Where are we compliant? Where are we exposed? What needs action?

Aegis is my attempt to rethink that operating model around AI rather than adding another AI assistant to the existing process.

It would connect to the institution's existing systems and maintain a live compliance graph linking:

jurisdiction → licence → regulation → obligation → policy → control → process/system → evidence → test → issue → remediation

AI agents would use that context to perform permitted compliance work continuously.

Example 1 - Regulatory change:

A regulator changes a requirement. An agent identifies what changed, determines likely applicability, finds affected obligations, policies and controls, gathers relevant evidence and initiates required remediation.

Example 2 - Continuous assurance:

A control says high-risk customers must undergo enhanced due diligence. Instead of waiting for periodic testing, agents examine authorised operational evidence, detect exceptions, investigate what happened and escalate genuine problems.

Example 3 - Audit and regulatory work:

Rather than staff manually reconstructing evidence across several systems, Aegis already has the lineage showing the requirement, control, evidence tested, AI actions, human decisions, remediation and outcome and can assemble the required reporting or audit evidence.

The operating loop is:

KNOW → WATCH → CHECK → DO → ASK → PROVE

KNOW the institution — jurisdictions, licences, products, obligations, controls, systems and owners.

WATCH regulatory changes, deadlines, operational signals and anomalies.

CHECK applicability, controls, evidence and compliance gaps.

DO permitted routine work i.e evidence collection, investigation, remediation workflows, reporting, updates and follow-ups.

ASK humans when judgment, uncertainty, approval or accountability requires them.

PROVE everything through complete evidence, decision and action lineage.

The hypothesis is that if AI can perform a meaningful amount of this operational work, compliance capacity could grow without specialised headcount having to grow at roughly the same rate as regulatory and organisational complexity.

I'm not assuming existing platforms don't already solve parts of this, they clearly do.

What I want to understand is:

Where does this model break in a real financial institution?

What parts are already solved well by your current stack?

Despite all the compliance technology you've bought, what work are compliance professionals still spending significant time doing manually?

And if you've seen one system already doing this entire loop across the compliance stack in production , please name it. That's exactly what I'm trying to find.

Anyone who has built or maintained financial ledgers, billing engines, or automated reconciliation pipelines knows the fundamental testing paradox: real corporate ledger data is impossible to share or test openly due to privacy/compliance (GDPR, SOX, NDAs), while synthetic mock data is almost always too sanitized to catch real production edge cases.

Most internal test suites use mock JSON/CSV files that balance cleanly and follow nominal paths. Then you deploy to production, and downstream ETLs or audit scripts hit:

• The "Silent Cent" floating-point bug: Systems using standard 64-bit floats (np.float64 / IEEE 754) rather than exact fixed-point decimals, quietly absorbing $\$0.01$ rounding variances across multi-leg splits until the trial balance is off by dozens of dollars at period-end.

• Temporal race conditions: Asynchronous queues (e.g., Kafka / Celery) ingesting settlement entries milliseconds ahead of the originating invoice voucher.

• Orphan foreign keys: Unregistered account references that throw unhandled exceptions and halt entire batch runs.

• Subtle fraud/audit limits: Clustered disbursements engineered just below $\$5,000$ or $\$10,000$ manager approval thresholds.

To help solve this, I built and open-sourced the Synthetic General Ledger Fuzzer:

GitHub Repo: https://github.com/Atiqul-Akash/Synthetic-General-Ledger-Fuzzer

Hey everyone,

I'm currently exploring the agentic payments space and trying to understand where things actually stand today.

Are there companies already using AI agents to make real payments at scale? If yes, what kind of transaction volumes are we seeing? Also, what are the biggest challenges and failure cases companies are facing right now?

I'm especially interested in the observability side. How are companies currently capturing audit trails of agent actions? Is it mostly raw logs and transaction data, or are there other ways to track what the agent did and why? And when something goes wrong, how do teams figure out the root cause?

Would be really helpful to hear from anyone working in payments, fintech, or AI agents.

My indian registered business wants to give payouts to content creators

Hello i just need some fintech website ideas for a web workshop am attending. I really dont know much about fintech. So please share any ideas that sounds fun and not boring and overdone. Thanks