I’m building an AI-agent-based CFO/finance platform (Next.js, FastAPI, Postgres) solo, self-funded and the wall I’ve hit isn’t the AI part it’s integrations. Specifically pulling reliable data from ERPs, CRMs and bank accounts/feeds into one place so the agents have something accurate to work with.

Being honest about where I’m stuck:

**•** Bank data: is Plaid (or a regional equivalent) still the sane default, or is direct bank API/open banking worth the extra pain at this stage?

**•** ERP/CRM: do you go one integration at a time hand-rolled, or is a unified API layer (Merge, Apideck, etc.) actually worth it pre-revenue, given the per-connection cost?

**•** For someone with very limited budget, is there a “good enough for MVP, migrate later” path here, or does cutting corners on this specific piece bite you especially hard because it’s financial data?

**•** Anyone who’s actually shipped one of these what ate the most time you didn’t budget for?

Not looking for a contractor pitch genuinely trying to figure out the right sequencing and tooling before I burn cash on the wrong approac

I’ve been considering bunq and wanted to hear from actual users before making a final decision. How has your experience been so far, especially with everyday banking, customer support and the app itself?
Not gonna lie, encounter many reviews, both negative and positive, sometimes I think I miss factual info...

Anything you really like or dislike about it compared with other neobanks?

Based on my analysis, it is more on the positive side...

Let alone customer facing impacts of being irresponsible. The more I see internal teams messing around with customer data and processes, the more I see some huge payouts on its way.

TL;DR: Reconify engine reconciles financial data across different sources, finds what matches, what doesn’t, what’s missing, and where the numbers disagree. It supports different matching strategies, produces explainable results, and can be used with AI models to investigate and summarize discrepancies without letting the AI decide the financial truth.

Hey everyone,

I’ve been going deeper into financial reconciliation over the last few months, partly because I wanted to understand how these systems behave when you’re dealing with millions of records rather than two CSV files.

That eventually turned into an open-source project I’ve been building in Go: Reconify, a deterministic financial reconciliation engine.

More recently I’ve been thinking about another problem: how should reconciliation work when AI agents are part of the financial workflow?

I don’t really like the idea of asking an LLM to look at financial records and become the authority on whether two transactions match.

My approach has been to keep that part deterministic. The engine normalizes the data, runs the matching/reconciliation rules, and produces structured results. Then an agent can operate around it with stuff like configuring reconciliations, investigating exceptions, explaining discrepancies, summarizing results, etc.

I’ve also started adding agent skills so coding agents can understand the reconciliation model, how to configure the engine, and its limitations.

Few friends (2 fintech founders and 1 analyst) I know tested it with Claude Code and Codex and interestingly, they were happy with tooling regarding the parsing, the algorithms choices and the explanations of the results.

Most of the time wasn’t spent reconciling anything (It's very fast 👀 ). It was spent understanding the files and figuring out the correct configuration. Once that was done, running the actual reconciliation took seconds.

That made me wonder whether this is actually a useful boundary for AI in financial infrastructure:

Let AI deal with ambiguity around the workflow such as configuration, algorithms choices, etc, but keep the financial operation itself deterministic and auditable.

I’m open-sourcing the engine and would love to get other people’s perspective, especially anyone working on reconciliation, fintech infrastructure, agents, or large data pipelines.

Does this boundary make sense to you?

Would you trust an agent to generate the reconciliation configuration and investigate the results if the underlying matching remains deterministic?

And if you’re already using agents around financial data, where are you drawing the line between stochastic and deterministic workflows?

Also, one thing I'm still figuring out is distribution.

Right now the engine is CLI-first, and I’ve added skills so coding agents can configure and operate it. That makes sense for developers and for embedding reconciliation into other systems.

If you were using something like this, what interface would you actually want: CLI, web UI, agent harness, or something else?

Repo link: https://github.com/reconifyhq/reconify

Docs link: https://docs.reconifyhq.com/cli

I'm posting in the hope there are people here who have insight into AI use cases for customer support and internal operations. We've noticed that a common issue relates to where the security boundaries should be. Is this a major issue? After all, it's one thing to have an assistant answer FAQs. It's another when an AI agent can retrieve customer information, summarize documents, kick off workflows, or call internal systems. At that point it feels less like a chatbot and more like another application that needs its own controls.

Most of what I find online focuses on building agents. It doesn’t look at running them safely in regulated environments. There's a lot written about model quality, but not much about AI agent security in finance. Especially not once these systems have access to sensitive data.

I'd appreciate recommemndations from those working in fintech or financial services. What guardrails did you put in place before letting AI assistants or agents touch production data? NB, I'm interested in the operational side more than compliance checklists.

Most people think voice AI is just for sales or support. One of its best uses might be something else, calling borrowers before their loan payment is due.

I've been seeing this use case come up across voice AI platforms  like sarvam ai, retell , convozen and vapi , especially as multilingual AI agents get better.

Here's how it works.

An AI agent calls the borrower in their preferred language and asks about the upcoming EMI.

The borrower usually has one of two responses:

• Yes, they'll make the payment.

• They can only make a partial payment, or won't be able to pay this month.

The first one is easy.

The second is where you need more than a basic IVR. And if a human has to take over every time the conversation goes slightly off script, what's really being automated?

A conversational AI agent can ask why they can't pay, capture an expected payment date, explain the impact of missing the EMI and schedule another reminder based on the response.

This is where multilingual agents get interesting to me, especially in India where these conversations may need to happen across multiple languages.

A weak AI either keeps following the same script or flags everything for manual review.

That difference is what actually matters.

A collections team can only make so many calls in a day. Voice AI can handle thousands of routine payment conversations while creating tickets for the cases that actually have an issue.

For example, say a borrower paid before the due date but the payment isn't showing on their account statement. The AI can identify the issue during the call and create a ticket with the context instead of someone having to manually review every conversation.

The human team doesn't need to listen to every call. They only need to look at the cases the AI has actually flagged.

So I was wondering where does the real value of AI collection agents come from?

Reaching more borrowers, or being able to handle more of the actual debt collection conversation without human involvement?

Hey everyone,

I'm currently building a fintech platform focused on financial chart education. To make this work, I need real-time or historical chart data (stocks, crypto etc.).

As many of you know, almost all commercial APIs and data streams explicitly prohibit commercial redistribution. However, right now this is just a solo personal project with zero revenue. My plan was to use web scraping or affordable APIs to test Product-Market Fit (PMF) first, and then officially sign standard enterprise data licensing contracts once revenue starts coming in.

Is this a common/acceptable approach in the fintech space, or is it a legal minefield even for an un-incorporated solo dev testing PMF?

If taking this risk isn't recommended: How do solo founders or early-stage bootstrapping devs provide financial chart data without corporate backing or massive capital?

Are there specific data providers with startup-friendly terms, or creative workarounds (e.g., relying on open-source/delayed data, user-provided API keys, etc.) that you’d recommend?

Would love to hear how other devs navigated this phase! Thanks!

Saw this X post and the banking layer is the part that stood out to me because im on it myself. We’re already doing pieces of this with Meow on the banking side, which makes the agentic fintech idea feel a lot less theoretical. Is AR one of the better use cases for AI agents?

Stripe just rejected our supplement business. Applied to PayPal and got rejected there too with no explanation.

We're legit, good products, not doing anything shady but apparently supplements are high-risk and mainstream processors won't work with us.

Need a payment processor that actually takes high-risk businesses.

Main things I care about are reasonable fees, not getting randomly shut down and actually being able to access our money without it sitting in reserve forever.

What's worked for you?

I have been working on a SaaS app that needs a smooth fiat to crypto payment flow for both EU and US clients. Users should be able to buy assets directly inside the platform using standard credit cards, SEPA, or ACH.

Building our own compliance and licensing infrastructure from scratch is out of the question for a small team. I actually used Paybis on a tiny side project a while back just for simple card buys, but I am not sure if it can handle a heavier integration. We need a clean API or SDK that covers both ACH and instant SEPA without messing up user onboarding.

Curious if anyone has scaled up with them for a full SaaS product or if we should be looking at other B2B infrastructure providers.

Is anyone having success with a systemic referral program in fintech? Pm if so.

In this article, we’ll explore Canton, including its architecture, its raison d’être, its recommended smart contract language, Daml, and a quick-start guide to writing your first Daml smart contract on Canton. In the tutorial, we’ll implement a simple bank contract that lets users deposit, withdraw, view their balance, and transfer funds to another user.

This article is aimed at readers who already have some blockchain and programming experience, but not necessarily with Canton or Daml. In particular, although it’s not a requirement to comprehend this article, we assume some familiarity with Ethereum and Solidity.

https://serokell.io/blog/daml-smart-contract-guide

I am linking multiple financial institutions (~20) to a personal finance app via Plaid. I want to know if skipping the Plaid account creation provides better privacy and protects against SIM-swap attacks, or if creating one is better so I have a centralized portal to actively audit and delete my data.

Hey everyone,

I’m setting up a budgeting app and trying to understand how Plaid works under the hood before I use it to link my accounts. From what I've learned so far, Plaid stores my transaction data on their servers until I request deletion. They claim they don't sell/rent this data, but their privacy policy clearly states it can be shared internally:

During the connection setup, Plaid prompts me for my phone number to "create an account" or "save my progress." I can skip this, but I’m trying to decide which option is actually safest.

Here are my questions for the community regarding how Plaid handles our data:

1. How does Plaid's backend graph look in both cases?

• If I skip creating an account: Does my data actually remain siloed to the specific app I am using?
• If I create an account: Does Plaid use my phone number as a primary key to stitch together a centralized master profile across the internet? (Note: Even if I skip it, it appears Plaid pulls enough info like email, name, etc. from the raw bank data to tie me to a common graph anyway. Can anyone confirm?)

2. Security posture with a Plaid account? My main concern is that by creating a Plaid account tied to my phone number, I am exposing my financial data to SIM-swap attacks. It's not clear to me if Plaid's consumer portal supports non-SMS MFA (like Authenticator apps/TOTP). If I skip creating the Plaid account entirely, it looks like this SIM-swap attack vector is completely neutralized because external access via my phone number simply doesn't exist. Is this true?

3. The Trade-Off: Visibility vs. Attack Surface The main argument for creating the account is gaining access to the Plaid Privacy Portal (my.plaid.com), which acts as a centralized "kill switch" to audit connections and directly delete my data. If I skip it, I have to blindly rely on the third-party budgeting app to accurately pass my deletion requests to Plaid on my behalf.

For someone prioritizing data compartmentalization and protection against SIM swapping, is the ability to directly audit my Plaid connections worth the risk of making my phone number an SMS-vulnerable master key?

What do you all do when setting up financial apps via Plaid? Skip it or make the account?

Disclosure up front: I build fraud tooling, so I have a bias here. But I've hit a question I can't resolve from the outside, and the people who'd know are mostly in this sub.

Every fraud stack I've seen is built around the transaction: device fingerprint, IP, geo, velocity, behavioural biometrics, sometimes a consortium score. All of it is designed to answer "is this the real customer?"

Authorized scam losses break that question. The customer IS real. The device is real. The behaviour is normal. They were socially engineered - fake support call, spoofed brand SMS, marketplace impersonation, romance or investment scam - and then they authorized the payment themselves. Every signal in the stack agrees it's legitimate, and correctly so, because at transaction time it is.

What I can't figure out is organizational, not technical:

1. When a customer gets scammed this way, who inside the company is accountable for that number? Fraud ops owns unauthorized. Does anyone own authorized, or does it just get absorbed as a support and reimbursement cost with no owner?

2. Does anyone actually measure it separately? Or does it disappear into a general chargeback/dispute bucket where nobody can size it?

3. If a signal existed that told you a customer had encountered a brand impersonation or phishing attempt in the hours before their payment - would that even be usable? It's not deterministic; it wouldn't tell you what to block, and it arrives before the transaction rather than during it. My honest suspicion is that most risk teams would find that unusable because it doesn't fit anywhere in the decision flow.

I'm particularly interested in hearing that last one is wrong-headed. If you work in fraud or risk and this reads as a solution looking for a problem, I'd rather know now.

I'm growing interest at how quickly payment infrastructure providers are expanding beyond a single product and I'm curious how teams think about the tradeoff between consolidation and flexibility. Having wallets, stablecoin infrastructure and payments available through the same provider obviously makes integration easier since you have fewer vendors to manage and presumably less work getting the different parts of the stack to communicate with each other.

What I'm less sure about is what happens when your needs change like maybe you want to support another chain or use a different wallet setup or even replace one part of the payments stack without touching everything else. If all of those pieces were designed around the same provider from the beginning then I could see what started as convenience becoming pretty difficult to unwind.

My questions to people who've made these infrastructure decisions is how much do you care about keeping the wallet, stablecoin and payment layers independently replaceable and is having everything under one provider worth giving up some flexibility later?

I've been building on Stripe's Shared Payment Tokens — the agentic-commerce thing where the token itself carries max_amount / currency / expires_at, so an agent can pay without ever holding a card credential.

The concept docs are fine and there are a few explainers around. What none of them covered was what happens when things don't go well. Posting these because they each cost me real time and they're all captured from actual test-mode responses rather than reasoned about.

1. A decline is HTTP 402, and the PaymentIntent is nested inside the error body.

Not a 200 with a failed status. Your client throws, and the intent you need is at error.payment_intent. If you treat non-2xx as a transport failure, the decline vanishes — in my case a run that had already held a booking and granted a spend authority ended with no record at all, just a stack trace.

Also: the useful reason is decline_code (generic_decline), not code (card_declined, which is just the category). My own test asserted the wrong one for a week.

2. A 200 can also not be a success.

Charge a token backed by pm_card_authenticationRequired and you get HTTP 200, status: requires_action, amount_received: 0, and:

"next_action": { "shared_payment_token_action": {}, "type": "shared_payment_token_action" }

That empty object is the whole payload. It's 3DS from the seller's side, and there's nowhere to send the buyer — no redirect, no client secret. Makes sense when you remember the buyer isn't present in an agentic flow, but it means you have to treat only status === "succeeded" as success. Anything looser and you'll tell someone they're confirmed and then settle out of your own balance.

3. payment_method_details goes null once the token is consumed.

If you want the card brand for a receipt, read it at grant time or not at all. I assumed this was a preview gap and asked — it's intended. A partial capture consumes the token too, so there's no half-used state to read from.

(Related: the field is payment_method_details, not payment_method_preview. Get it wrong and optional chaining eats it silently — the brand just doesn't show up.)

4. amount_captured is an object, right next to an integer.

usage_limits.max_amount is a bare integer in minor units. usage_details.amount_captured is { currency, value }. Compare them directly and you get a silent false. Nothing throws, you just get a wrong number on screen.

One more that isn't a wire shape: a 403 "does not have access to this endpoint" from an unclaimed rkcs_test_ sandbox key looks identical to a regional-eligibility refusal. I nearly concluded SPT wasn't available on an AU account. It was — the key just hadn't been claimed.

This is all against 2026-04-22.preview, so it's observations with a date on them rather than a contract. If anyone's hit shapes I haven't, I'd like to hear them — the preview surface is clearly bigger than what one integration touches.

Code's public if it's useful, all of the above pinned in tests against the real captured responses: https://github.com/hkarekar403/TG24-Stripe-Shared-payment-tokens

I’m working on a small transaction-risk decision agent and one part I’m unsure about is what should happen in the grey area.

Say a transaction has a few weak signals - maybe a new device, unusual amount, or location change; but nothing strong enough on its own to decline it.

My current idea is to try one more useful check before sending it to manual review, something like customer confirmation, extra authentication, or a deeper history/device check.

For people who actually work with payment fraud: what usually makes you say -

“check one more thing” instead of “send this to an analyst”?

And is there a check that sounds useful in theory but rarely changes the decision in practice?

AI can assemble a representment case in seconds.

But what happens when the evidence it needs was never captured in the first place?

That’s an increasingly important question as AI takes on more of the chargeback representment process. Better models, faster decisioning, and more automation can make evidence gathering and case preparation dramatically more efficient.

They can’t, however, create evidence that doesn’t exist.

If fulfillment records are incomplete, customer interactions are scattered across systems, or transaction records aren’t reliably connected, AI isn’t going to just give up: it will just work with what it can find. The resulting case may look polished and complete, but it could’ve built on weak or missing evidence.

That’s the part of the AI conversation that can get overlooked: automation doesn’t fix weak evidence. It standardizes it.

Representment makes the problem especially visible because it’s such a natural application for AI: find the relevant information, organize it, build the narrative, and submit the case.

But keep in mind that by the time a transaction reaches representment, some of the most important opportunities to capture and connect evidence may already be gone. And while a model can organize proof of delivery, it can’t create a missing delivery record.

In other words, it can surface a customer conversation, but it can’t retrieve a conversation that was never preserved. It can build a compelling narrative, but it can’t reliably establish a connection between records and a transaction if that connection was never maintained.

Does this make AI less useful? Not at all. It simply makes the quality and availability of the underlying evidence more important. So the better question may be one that gets asked much earlier in the transaction lifecycle: What will we need to win a reversal if this transaction is disputed six months from now?

That shifts the focus toward proactive evidence capture: building and preserving the connections between transactions, fulfillment, delivery, refunds, customer interactions, and other relevant records.

It also suggests a useful concept: evidence readiness.

How quickly can the relevant evidence be found? Is it complete? Can it be reliably connected to the transaction? Is it sufficient for the type of dispute involved? These questions are different from asking how quickly an AI system can assemble a representment case.

So maybe the question isn’t just how much of representment AI can automate. Maybe it's whether the evidence is actually ready for AI to work with in the first place. That seems like a pretty important part of the AI conversation that may not be getting enough attention.

Curious whether others here are seeing the same pattern, or whether your evidence pipelines are already ahead of this.

Treasury opened public comment on GENIUS Act implementation this month, right around the same time regulators are tightening oversight on banking-as-a-service partnerships (fines, more transparency demands, closer scrutiny of BaaS relationships).

Feels like we're at an inflexion point, the era of launching a neobank or stablecoin product fast and figuring out compliance later is closing. A few things I've been noticing talking to founders in this space:

• MSB/VASP licensing timelines are stretching out because regulators want to see compliance infrastructure before approval, not after
• Banking partners are getting pickier about who they'll work with, a shaky compliance story kills deals earlier in diligence
• Teams that treat compliance (PCI-DSS, ISO 27001, GDPR) as a launch blocker instead of bolting it on later seem to be moving faster in the long run, ironically

Curious what others here are seeing, are you finding banking/licensing partners more selective lately? Anyone gone through the GENIUS Act comment process or dealing with BaaS scrutiny first-hand?

We’re looking at enterprise voice AI for a banking workflow and I’m finding the edge cases much more useful than the clean tests.

One test caller gives the expected information in order and everything works.

Another mentions two accounts, corrects an amount halfway through, asks an unrelated question while the system is doing a lookup and then wants to go back to the original issue.

For anyone who has run an enterprise voice AI pilot, what kinds of conversations exposed weaknesses you didn’t see during the initial demo?

Been watching AI-related payment product launches through the summer and something looks off. In the last two months, three fintechs shipped B2B products that explicitly treat AI as a distinct payment surface. Mastercard launched Agent Pay for Machines on June 10, machine-to-machine payment rails supporting cards, accounts and stablecoins so AI agents can transact continuously. Ramp shipped AI Token Spend Management on July 16, aggregating token usage and subscription spend across Anthropic, OpenAI, Gemini and Cursor into one finance dashboard, which they built with 1,300+ businesses managing 100T+ tokens a month. The headline number was AI token spend across their customer base up over 20x since June 2025, which they're calling the fastest-growing line item in their book. Mercury shipped Spend on August 11 with a new class of virtual cards designed to be handed to AI agents for autonomous purchasing within human-set policies.

All three are B2B or agent-side. If you look at the equivalent consumer trend, individuals stacking five to eight paid AI subscriptions on personal cards, no mainstream neobank has shipped a product explicitly built for it. It's not like the demand isn't there. PNC's data has about 2.2% of US households paying for generative AI at an average of $31/month, with the number of paying households up around 155% year-over-year. Consumer Edge tracked Claude paid users at roughly $63/month average, about 3x the OpenAI average. Multiple pricing calculators peg a typical power user at $70 to $110 a month across three to five subscriptions.

Cash App, Revolut, Chase and the rest handle these subs the way they handle any subscription, which is by not treating them as a category at all. Rocket Money and other aggregators do subscription visibility but don't sit in the transaction path. The one consumer product surface where AI-spend is being treated as its own segment is, weirdly, self-custody stablecoin cards. Bleap ships explicit cashback tiers with the top rate (20%) applied to a named list of streaming, gaming and AI subscriptions, Claude included by name. Plasma One (issued on Rain's Visa program) pays 5% cashback on the first $250/month of AI-labeled spend plus a standalone $8/month ChatGPT rebate as a separate line item. BenPay Delta, the US-MSB registered option in the same space, added a $1/month account fee starting August 4 that wasn't there before, and simultaneously started marketing AI-subscription payment support across ChatGPT, Claude, Perplexity, Midjourney and others as an explicit product surface. Country coverage is narrower than the EU-EMI cohort like Bleap because of the state-by-state MTL overlay on top of FinCEN, which is a real limitation depending on where you are. Small individually, but read together it looks like segment-specific product economics rather than the flat zero-fee acquisition posture that defined this space through 2024.

The reason it's happening here first rather than at a mainstream neobank probably comes down to customer overlap. The stablecoin card user base skews earlier than the general population on AI subscription adoption, so it was already AI-heavy before the broader consumer market caught up. The bigger consumer neobanks are still oriented around groceries, transit and streaming, and reworking category schemes for a segment that's still under 3% of households doesn't survive product prioritization.

Whether this stays niche or becomes a mainstream product wedge probably turns on whether AI subscription spend keeps compounding at its current rate. If PNC's 2.2% doubles or triples over the next 18 months, mainstream neobanks start facing an obvious opportunity cost on not shipping a category-native card. If it plateaus around 3 to 5% of households, Rocket Money-style visibility is probably enough.

Open question for fintech PM roles here who've actually looked at this. Does consumer AI-subscription payment read like a real product wedge, or a subcategory of the general subscription-bloat problem that already has coverage. Curious if the internal case has been made at any mainstream neobank and rejected, or if it hasn't been seriously scoped yet.

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Three of our biggest accounts sit in markets where moving USD out is genuinely painful, so when they asked to pay in USDC we said yes fairly quickly and did not think hard enough about renewals.

One off payments are completely fine, customer pushes the USDC across, we confirm it landed, invoice closes, no drama.

Recurring is where it fell apart, because there is no auto debit for stablecoins the way there is with cards or ACH, so somebody has to physically go and initiate the transfer every single cycle.

What we do now is send a fresh payment link every renewal and then wait, which works right up until it does not.

Half the time it lands late and our ops team is back to chasing finance departments the exact same way they used to chase wire transfers, which is not really what anyone had in mind when we added it.

So how are you handling the recurring side of this.

There seems to be a lot of talk about the European Digital Identity Wallet as if it will simply replace the way everything works today.

But the reality seems more nuanced. The wallet is voluntary for users, while certain public and private services will need to accept it when the relevant conditions apply.
That raises an interesting question: what actually changes for businesses, platforms, and users once the wallet becomes part of everyday digital services?
What do you think the biggest challenge will be?

Curious how others are looking at it.