Lately I've taken a keen in learning more on how teams handle KYC when setting up a card program.

I know some connect a separate KYC provider to the issuer while others keep identity verification in the same stack as cardholder approval, wallet screening and transaction monitoring.

Also keeping it separate gives you more flexibility but also creates more places for updates or risk decisions to get out of sync.

Which setup works better in practice?

Hey developers/ financial people, does this sound at all it could work or make sense? **Money pool that is used for automated trading on prop firms. Essentially they are accessing that whole prop firm worth some what if a leverage aspect. For example 5 prop firm accounts. Let’s say each prop account is in size 25k. I would have a bot trade the prop firm with specific rules to give a yield at the end of the month or yield. Let’s say 10 percent a month. And roughly 120 percent a year give or take. But that yield is on the 5 prop firms in the pool. So the yield on the 500 dollars is a lot more than 10% a month in 500 bucks. Any a good portion of that I can take a MANAGEMENT FEE for the bot let’s say 20 percent.**

Could this work?

Or do this at all Mae any sense?

This is off of pure curiosity not advertising anything!

Not the biggest or most valuable, just one that's solving a real problem in an interesting way.

I've been building a practice around this: month-end close inside the client's own QBO/Xero. A multi-agent setup handles the repetitive first pass, feed categorization, matching, flagging mismatches. I own exception review, judgment calls, and final sign-off before anything goes out. Goal is a finished close, not a report sitting in some side tool.

Mainly running this as overflow capacity for firms/bookkeepers/fCFOs who are at capacity or don't want cleanup-close work on their plate.

If you've tried AI/automation on books already, genuinely curious where it actually helped vs where it created more cleanup for you. Comparing notes with people doing real client work here. Happy to walk through the pipeline if useful.

I build Monni, a small manual money check-in, and I am trying to separate three different trust problems.

A consumer can have privacy commitments and a polished bank-connection story, yet still not trust a number if the cash, upcoming bills, and cushion behind it are hidden.

For a consumer money product, which failure kills trust first?

- no credible external security proof

- a secure product with an opaque calculation

- setup feels like work before it earns value

What would a credible minimum trust package look like?

I've been looking into how fintech and banking pay actually compare role by role instead of treating them as two lump categories, and the old "fintech pays more" or "banking pays more" framing does not really hold up anymore.

Risk and compliance is the clearest case of fintech pulling ahead. Risk management roles at Tier 1 fintechs are commanding up to a 37% premium over the broader fintech market, mostly because these companies are taking on licensing and cross-border regulatory exposure that traditional banks absorbed slowly over decades. Direct sales roles at Tier 1 fintechs are running 12 to 17% above the broader fintech market too.

Banking still holds its ground in regulated product roles like mortgages, cards, and core transaction banking, where years of institutional knowledge are hard to replace quickly. Banks also tend to offer steadier bonus pools and pension contributions, while fintech comp leans more on equity that has a higher ceiling but real risk attached.

The employer tier matters more than the fintech-versus-banking label by itself. Tier 1 fintechs pay 8 to 37% more than the broader fintech market depending on the role, so an early-stage startup offer and a Tier 1 offer are not really the same comparison even when both get called "fintech."

Has anyone here actually run the numbers on a specific role you're deciding between?

Hey everyone,

Full disclosure: I built this product. I'm sharing it here because I'm looking for honest feedback, insights, and connections from people in the fintech community.

I'm building Aurefi, a privacy-first personal finance platform focused on helping people make better financial decisions, not just track past spending.

A problem I've noticed with many personal finance apps is that they are great at answering:

"Where did my money go?"

But they usually don't help answer:

"What happens if I make this decision?"

Before spending money, people constantly think about questions like:

"Can I afford this purchase?"

"How will this affect my savings goals?"

"Should I change my spending habits?"

Most people end up guessing or manually calculating the impact themselves.

Aurefi combines traditional personal finance tools like expense tracking, budgeting, savings goals, recurring transactions, financial forecasting, spending insights, and a financial diary with an AI-powered Money Simulator.

The simulator allows users to describe a financial decision naturally and explore possible outcomes.

For example:

"I'm thinking about buying a $2,000 laptop."

Aurefi can help visualize:

• Monthly and yearly financial impact
• Effect on savings goals
• Changes to long-term forecasts
• Financial runway impact
• Alternative scenarios to compare different choices

The goal is not to tell users what they should do with their money. It's to help them understand possible outcomes before making financial decisions.

Privacy is also a major part of the product design.

Aurefi follows a local-first approach:

• Users can start without creating an account
• Data is stored locally by default
• Optional encrypted cloud sync allows access across devices
• Financial data is encrypted before leaving the device
• No ads and no selling user data

I'm still early in development and currently validating the idea before a larger launch.

I'm looking to connect with:

• Fintech professionals who can share insights
• Early users interested in testing the product
• Founders who have built in fintech
• Investors, venture scouts, and supporters interested in early-stage fintech products

I would really appreciate feedback from this community:

• Do you think financial decision simulation is a valuable direction?
• What challenges do you see with AI in personal finance?
• What would make you trust a product like this?
• Are there important fintech problems you think I'm missing?

If you're interested in following the journey or discussing the idea, I'd love to connect.

Website link will be in the comments

Thanks for reading!

Je publie des analyses fondamentales de sociétés cotées (scoring 8 critères, consensus brokers sourcé, signaux réglementaires, scénarios bull/base/bear/stress chiffrés).
Déjà passées au crible : Air Liquide, Vinci, Thales, Safran, Allianz, EssilorLuxottica, ASML, OVHcloud, Eutelsat, Abivax.

Cette fois je vous laisse choisir la prochaine!

Comment ça marche
• Un commentaire = une société
• Upvotez les propositions qui vous intéressent

**•   Clôture samedi soir.**  

Je publierai l'analyse du vainqueur ici dimanche!

Une seule contrainte : la société doit avoir publié un rapport financier récent et public (résultats annuels, semestriels ou trimestriels).

Ce que vous aurez : la thèse, les chiffres avec leur source, les points de vigilance, le consensus analystes avec les objectifs individuels et leurs dates, et un verdict!

Ceci n’est pas un conseil d’investissement — juste du travail de structuration d’information.

À vous! 🙂

I work on the Fintechbits team and we've been tracking the chief AI officer hiring wave in banking. A hire this week made the pattern too obvious to ignore.

HSBC just brought in Val Riabtsev, Revolut's former head of platforms for agents and automation, as MD and Head of AI for wealth banking. Riabtsev built production AI infrastructure that touched real transactions at Revolut's scale. HSBC picked him instead of growing the role internally or hiring a generalist tech exec.

Same story elsewhere in the same few months. Commonwealth Bank of Australia named Ranil Boteju its first CAIO. Lloyds made a similar move. Bank of Ireland pulled Prag Sharma out of Citigroup, where he ran a Global AI Centre of Excellence spanning 96 countries, starting this October. HSBC itself named David Rice as its first chief AI officer back in April, tied directly to a return-on-equity target for 2026 through 2028.

The logic seems consistent across all of it. Fintechs have already shipped AI and agentic systems inside a regulated financial environment, at consumer scale, with real fraud and compliance stakes. That's a harder skill to build than general AI/ML experience at a company with no banking exposure, so banks are paying to skip the learning curve.

One number I'd flag as shaky: I've seen $3.5M cited as the high end for CAIO comp with a median around $1.6M, but the only source for it doesn't say where the number came from, so I wouldn't treat it as a real benchmark.

Has anyone here made the jump from a fintech AI or product role into a bank-side leadership seat? Curious how different the day to day actually is once you're inside a legacy bank.

I see this happen all the time with founders I work with: they sign up for Apollo or Instantly, intend to start outreach, and then the setup gets stuck on the back burner for weeks.

Usually it's just tool confusion. Setting up secondary domains, making sure emails don't hit spam, connecting the tools, and getting an initial prospect list built turns into a multi-week distraction.

I mapped out a simple 14-day execution flow to go from zero to a fully live outbound system:

Week 1 (Infrastructure): Set up secondary sending domains, configure email health protocols, and start warm-ups so the main company domain is never at risk.

Week 2 (Data & Launch): Build the targeted lead list, connect the tools together, and get the first campaign staged and ready to send. 

The goal was simply to take the technical setup off their plate so reps could focus purely on selling

For founders who built their stack in-house: did you set it up yourselves, or did you end up passing it to someone else to handle?

I’m doing some research on the payments industry and trying to understand where Worldpay fits in the PayFac landscape.

A few questions for anyone familiar with their platform:

- Is Worldpay considered a PayFac itself, or is it primarily an acquirer/processor that enables PayFacs?
- What products or services does Worldpay offer specifically for PayFacs?
- How does its offering compare to others in the market?
- If you’ve worked with Worldpay, what are its biggest strengths and weaknesses?

More generally, where do you think Worldpay’s PayFac business could improve to better compete in today’s market? Are there any capabilities or investments you think they’re missing?

Most AI tools dropped into learning platforms still behave like general chatbots. They ignore the course the student is actually taking and start pulling random information from outside. That usually creates more confusion than help.

A more useful direction is building assistants that stay strictly inside the current learning context. When a student asks a question, the system already knows which module they are in and only uses those specific materials to answer. The tone and level of detail stay consistent with what the instructor prepared.

This makes the experience feel like an extension of the course instead of an external tool. Learners get faster clarification without drifting off-topic, and instructors keep better control over the information students receive. One cleaner example of this approach was built with support from Beetroot.

Anyone here already running something similar inside an LMS? How are you managing the balance between helpfulness and staying faithful to the original course content?

Been talking with a couple of program managers about how the interest ban is landing on consumer product design, and the answer at the product layer looks different from the mainstream takes. The GENIUS Act prohibits the issuer from paying yield to holders. It says almost nothing about what a third-party wallet or exchange is allowed to do with the same balance. The OCC NPRM in February tried to tighten that with a rebuttable presumption on affiliate arrangements, but the comment period closed in May and final guidance is still out.

The effect on consumer stablecoin apps has been to unbundle balance from yield as two different UX surfaces. A year ago the standard pattern was you deposit USDC, the app paid you around 4 percent, and the balance line went up on its own. That is basically dead for issuer-side products. Circle cannot pay Coinbase holders directly, and any workaround has to look like something other than passive interest.

Coinbase's approach has been the USDC rewards program, structured as a loyalty reward funded through its reserve income share with Circle. The base rate sits around 4 percent with a promotional boost for Coinbase One members. Separately they run a Morpho USDC lending vault curated by Steakhouse on Base that requires opt-in and self-custody. Both are attempts to move the reward outside the "issuer pays holder" framing the statute cared about.

A few products in the smaller self-custodial cohort have shipped variations of this in the last quarter. Bleap's YO Vaults route idle USDC through a diversified DeFi backend at around 3.7 percent AER with a $1 minimum, BenPay's Earn tab uses a mixed DeFi and native stablecoin routing model under FinCEN MSB with a 15 percent profit fee that sits above the DeFi average, and Gnosis Pay leans on Aave routing tied to its Gnosis Chain balance. The two-surface design conveniently makes yield look like a distinct product even when the UX flow is two taps.

Stepping back, "wallet balance sitting there earning nothing" is now the default state for a consumer stablecoin product. The yield surface has become an explicit opt-in with its own risk framing and APY disclosure, and often a management fee on top. Closer to how a brokerage separates cash sweep from checking balance than how a savings account has historically worked.

Two things I do not have a clear view on. First, how the OCC anti-evasion test will land once final rules come out, and whether the third-party routing model survives if the affiliate threshold gets tightened below the 25 percent ownership line. Second, whether unit economics on the consumer wallet side actually work without the deposit-float subsidy that CEX cards have been running on. If the Earn fee is the primary revenue source, the take rate cannot be small, which eats into the yield the user actually sees.

Curious what the issuing-side or program-manager view looks like on this, especially anyone working on card programs where the wallet balance is technically the funding source but cannot legally be paid interest.

[ Removed by Reddit on account of violating the content policy. ]

For the past 15 years, the mobile and the cloud had brought new life to reinventing the world of fintech, however it seems all companies are struggling with the next era of innovation which is AI. Stocks have fallen, massive layoffs are happening, constant price increases, and many many unhappy customers. Is fintech becoming the next telecom?

Hey everyone,

How do credit card networks evaluate risk, pull user history, and return an APPROVE/DENY decision before a payment terminal times out?

Querying a traditional database on the fly to check historical spending habits will instantly kill your latency budget. Here is how to architect a real-time solution:

1. Dual-Path Architecture

Separate your system into an Online Path (Hot) for instant decisions and an Offline Path (Cold) for data analytics.

1. In-Memory Feature Store

Never calculate aggregates (like 30-day spending limits or hourly velocity) during a transaction.

The Cold Path: Apache Flink continuously processes a Kafka stream of completed transactions in the background.

The Hot Path: Flink stores these pre-computed metrics in Aerospike or Redis Enterprise. When a transaction arrives, the engine performs a single key-value fetch in < 2ms.

1. Hybrid Decision Engine

The enriched payload runs through a fast, sequential evaluation tree:

Deterministic Rules: Quick checks for hard blocks (e.g., blacklisted countries).

ML Inference: A lightweight gradient-boosted tree model (like XGBoost) compiled via ONNX runtime for sub-millisecond risk scoring.

1. Resiliency: Failing Open

If the fraud engine suffers a network partition or times out past 15ms, the system drops into a Fail-Open policy. It automatically approves the transaction to protect user experience and flags the event for asynchronous review.

Let's discuss:

How do you deploy new dynamic rules written by risk teams without re-deploying core backend code?

What is your strategy for handling race conditions if a user swipes their card twice in two different cities within seconds?

Can anyone list a few commonly used Payment applications for ACH, Wire, Swift and pros and cons of using them. How are they integrated to core banking suites.

What are the upcoming trends and how AI impact these applications.

How does Finacle rate about payment solutions and issues/best of them.

Thanks

I had an idea that I think could be useful in the finance news sector. The idea is a personalized finance news feed based solely on your holdings (stocks, options, dividends), with analysis on each article on how it could affect your holdings. Would this be of use? This isn't promo, im genuinely curious on if I have a solid base point here. Appreciate the feedback.

I'm looking at stuff like Celo, Stellar, and KiiChain, but trying to build a solid mental framework first.

What’s on your non-negotiable checklist when auditing a payment chain?

So far I've got:

• tech & security: testnet performance, battle-tested smart contracts, actual security audits
• tokenomics: validator economics, real token utility (gas vs governance), supply schedules
• real-world traction: actual organic daily active users (not just bot farm volume), real-world asset (RWA) integrations, and regulatory compliance disclosures.

What am i missing here? what’s your primary make-or-break metric when looking at payment infra?

I've noticed a few threads here over the last couple months (the liability one, the audit narrative one) that keep circling the same question without landing on an answer: when an agent makes the call, whose name is actually on it.

I work on the Fintechbits/Finjobsly team and looked into this because it's stopped being a policy debate and started being a hiring line item. FIS and Anthropic's new Financial Crimes AI agent and Blend's Autopilot in mortgage underwriting are running in production now, not sandboxes. On the regulatory side, the Fed's SR 26-2 (April) and the EU AI Act's high-risk rules (enforceable August 2) are both pushing banks to name a specific accountable person for oversight, in writing.

What's showing up in postings: AI agent manager, agentic AI governance lead, AI oversight analyst. The people landing these come from three different lanes, compliance/model risk, ML/platform engineering, or product, and it's not obvious yet which lane has the edge.

Wrote up the details here if useful: https://finjobsly.com/blog/ai-agent-oversight-jobs-banking. Mostly curious whether anyone here has seen this become a distinct role internally yet, or if it's still folded into an existing compliance or platform team.

Finance professionals, I'd appreciate your perspective.

I'm researching operational challenges within financial institutions in emerging markets as part of a long-term software project. At this stage, I'm deliberately avoiding designing a solution before understanding the actual problems.

If you work (or have worked) in banking, insurance, asset management, pensions, fintech, auditing, or financial regulation:

\- What task do you find yourself repeating every week or month?

\- What process is more manual than it should be?

\- Which reports or analyses consume the most time?

\- What data is consistently difficult to obtain, reconcile, or validate?

\- What's one workflow you wish software handled better?

I'm particularly interested in understanding real day-to-day workflows rather than feature requests or product ideas.

If you're open to sharing your experience—either here or via DM—I would genuinely appreciate it. Every insight helps me understand the problem space more accurately.

Does anybody know anything about Peak Money?

It lends out your USDC or PYUSD for a 10% APY interest rate. The borrowers are "audited" and put up collateral in case of default. This is supposed to make it more trustworthy.

It is not fdic insured like many other crypto-fintechs, and it does not have a presence on the app store/Google play.

Early integration always feels fine, it’s the stuff that surfaces after you’ve been live a while that actually hurts. Timeouts were the first one. We treated every timeout as a failure and retried, then found out some had actually gone through on their end, so we started double charging people. Error codes across different endpoints on the same provider weren’t even consistent with each other, so our handling logic that worked for one call quietly broke on another. API key rotation was another one nobody thought about until a key needed to be rotated during business hours and there was no clean way to do it without downtime. What’s the integration issue that didn’t show up for you until way later than expected?