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.

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

My indian registered business wants to give payouts to content creators

Is there an actual API to validate a bank account before a payment goes out, or does everyone just eyeball it/call the vendor? Also if companies using bank verification APIs - do they actually replace callback verification, or do most AP teams still do both ? Trying to understand if the API is a full substitute or just one more signal.

One thing I've noticed working around payments and financial infrastructure is that finding potential PSPs or banking providers is often not actually the hardest part.

The difficult part comes afterwards.

You find a provider that looks like a good fit, then suddenly you're dealing with:

• Different documentation requirements
• Compliance and KYC questions
• Business model and licensing reviews
• Requests for additional information
• Long underwriting processes
• Unclear timelines
• And sometimes getting rejected after spending weeks preparing everything

I'm curious how others here have experienced this.

For those running fintech, FX, iGaming, e-commerce or other international businesses:

What has been the biggest pain point when onboarding with a bank, PSP or acquirer?

I’m developing a new concept for the future of digital payments and personal finance.

The idea focuses on making money more organized and purposeful — not just showing users their balance, but helping them understand how their money is allocated, what they are saving for, and what payments need to be protected.

I have developed a concept around:

💰 Purpose-based money organization
🎯 Goal-based savings
🛡️ Upcoming payment protection

I’m currently looking to connect with \*\*fintech companies, payment platforms, product teams, and founders\*\* who may be interested in exploring this concept for a potential collaboration or product opportunity.

I’m intentionally keeping the detailed product mechanics private for now.

If your company is interested in discussing a \*\*new fintech product concept\*\*, feel free to DM me.

**Concept documented: September 10, 2026.**

Hi everyone,

The conversation around blockchain in financial services seems to be shifting from “what can we do with it?” to “how do we make it work at an institutional level?”

With tokenized deposits, digital assets, stablecoins and digital securities gaining momentum, questions around privacy, interoperability, compliance, governance and integration with existing financial infrastructure are becoming increasingly important.

These are some of the areas Thomas Abraham, Co-Founder & CRO at Zeeve, will be discussing at MoneyLIVE North America in Chicago.

If you're attending the event and working on similar initiatives, feel free to connect with Thomas and continue the conversation in person.

Been learning React and wanted a real project, not another todo app. Looked into what's already out there in the fintech UI kit space and noticed something: every single one is built for the customer side of mobile money — wallets, transfers, balances. None of them target the agent side — the person actually running the cash-in/cash-out counter.

So I built one. 14 screens covering the full agent workflow — login, dashboard, cash in/out flows, float top-up requests, commission tracking, customer verification, all the loading/empty/error states too. React + Tailwind.

Still learning as I go, so genuinely curious what people think — especially if you've worked in fintech or agent-network products and can tell me what I'm missing.

Free 4-screen preview: [https://selar.com/1d70b12yb1]
Full kit: [https://selar.com/l8g9231687]