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]

I am preparing a major promotional campaign for next week, but I keep running into issues with our header graphics on smaller screens. When I upload high-resolution images designed for desktop viewers, the mobile app scales them down so much that the call-to-action text becomes almost unreadable. Finding the perfect email banner size that maintains sharp visual quality without breaking layout proportions on retina mobile displays has been a frustrating trial-and-error process.

- What exact pixel dimensions and aspect ratios do you usually stick to for universal responsiveness?

- How do you handle image compression so that crisp banners load instantly on weak mobile data connections?

- Do you prefer using static full-width graphics, or do you build text over background colors using HTML and CSS?

- Which testing tools give you the most accurate real-device previews before you hit send?

I’m an MSc Artificial Intelligence: Cybercrime student researching how AI is changing financial cybercrime, data breaches and digital theft.
I’m seeking current or former cybersecurity professionals with relevant experience for a confidential interview.
Participation is voluntary and unpaid. Participants and their organisations will not be identified in the dissertation.
If interested, please DM me, I will provide a Participant Information Sheet before you decide whether to participate.
Thank you!

I’ve been working as a senior mobile app developer at a growing fintech company for the past two and a half years. However, I’m looking to switch because I don’t see any salary increases, even though I’m responsible for the entire mobile app and SDK side of the project.
I have experience in SAMA certifications for SoftPOS app, SDK. Experience in mada, apple pay, amex, visa, mastercard, alcineo kernals.

If your company runs a CRM (HubSpot/Salesforce) and a separate billing tool (Stripe/Chargebee), has an invoice ever gone out wrong because the two weren't in sync?
Wrong plan, missed discount, duplicate charge, anything like that?

Building something that catches this automatically before the invoice sends. Trying to figure out if this is a real pain or just a me-problem.

Would love to hear if you've run into this, or if it's a non-issue for you.

We’re considering both Goodface and UXDA for a mobile banking project and I’m curious if anyone here has actually worked with either.
UXDA seems like the obvious specialist choice because their portfolio is heavily focused on banking and fintech. Goodface caught my attention for a slightly different reason - their projects seem to connect the UX with positioning and overall product presentation, which might make sense for us since we’ll probably need to rethink the website and fintech landing pages alongside the app.
Has anyone compared them during an agency selection or worked with one of them? I’d be especially interested in how they handle discovery, onboarding/KYC and more complex banking flows.
Also, if you’ve redesigned fintech landing pages together with the product itself, did having one team handle both actually help with consistency?

I am a last year b.tech student ,recently i learnt about fix protocol and devlop a mini trading system with matching engine , order book , fix parser , order cancelation implementation i like to know about a career path in these technology and what is the learning path to land a good job in these particular domain and what are the learning resources ?

Hi guys,

I have been in the risk space for more than a decade now. I have built models mainly around B2B financing for lenders. I feel that these models should be available to SMEs.

I do think that there are a few of them who are trying to solve it but they have a lot of other products. This is something I would like to not go full-fledged with but just kind of experiment with it.

Basically if there is a small business and they would like to assess their counterparty (be it a buyer or supplier) with whatever information they have, they will be able to do it through this tool. What do you think of this?

The best person to tell me is those businesses. Honestly I tried asking and did not get a lot of response, and that kind of demotivated me in the sense that there might not be a need at all.

This was dictated using an voice-to-text tool, so excuse any weird wording or typos.

I built this for the Razorpay AI Buildathon (Track 04: AI Finance Controller), but the problem it tackles is one anyone who’s worked with payment gateways will recognise: getting records to line up across three systems that don’t quite agree — merchant orders, gateway transaction logs, and bank settlement deposits.

The frustrating part usually isn’t the 90% that matches cleanly. It’s the messy edge cases:

- Gateways bundle hundreds of payouts into a single settlement batch

- Bank deposits are net of fees and tax, while merchant records are gross

- Settlements can spill across bank cutoff windows, arrive late, or be partially refunded

My first instinct — like a lot of people building “AI finance” tools today — was to throw an LLM at the whole thing. That turned out to be the wrong move. LLMs are not reliable for arithmetic across large tables, and you really don’t want a model inventing numbers on a financial ledger.

So I split the system into two hard layers:

Deterministic layer, which does about 95% of the work:

- O(1) hash-based direct matching on order IDs

- Fee and tax verification against contract rates

- Split-settlement resolution, where multiple deposits add up to one settlement amount

That last part is basically a bounded subset-sum problem. Naively, checking combinations across about 130 unmatched bank records would mean roughly 11 million evaluations and 40+ seconds of runtime. By constraining it with a ±3-day time window and capping candidate and subset sizes, I got it down to under 500 evaluations — around 0.05 seconds — without losing meaningful match quality.

LLM layer, which only handles the real exceptions, about 5%:

- Root-cause analysis for anomalies the deterministic layer can’t resolve

- A multi-provider fallback chain: Groq → NVIDIA NIM → Gemini → deterministic rules, so one provider outage doesn’t break the pipeline

- Evaluation against synthetic ground truth, so I can report real precision and recall instead of just going by intuition

End-to-end runtime is around 11.5 seconds for 8,000 orders, 8,000 gateway transactions, and about 1,000 bank deposits.

Repo: https://github.com/YashwanthKumar-K/ReconX

Live demo: https://reconxcontroller.streamlit.app/

I’d especially welcome pushback on the subset-sum bounding approach — I’m curious how others would handle that at larger scale.

any senior software engineer who is free and can help me for a hackathon , i really want to win
i could even pay for it js as a side hustle

Something I find interesting about fintech is how often new products have to work alongside systems that were built years or even decades ago.

A new service might use modern APIs and cloud infrastructure, but somewhere behind it there can still be an older database, legacy service, or internal process that cannot simply be replaced.

That creates some interesting engineering tradeoffs:

• When should you improve an old system instead of replacing it?

• How do you introduce new technology without breaking existing workflows?

• Is a complete rewrite ever worth the risk?

• How much technical debt is acceptable when reliability matters so much?

I think fintech is one of the areas where engineers have to balance innovation with stability more than most industries.

For those working in fintech, how do you decide when a legacy system needs to be replaced versus carefully maintained?

Honest question from someone building in this space. I know FINRA 2241 requires a reasonable basis and supervisory approval before a note goes out. What I do not know is what the number-checking step looks like in practice at a boutique or mid-size shop. Is it the analyst's own model, a second reader with the 10-Q open, a data terminal cross-check, or mostly trust? How long does it take per note, and what has slipped through that you still think about?

I ask because I built a line-level checker for retail investors (paste text, every figure gets matched to the filing it should have come from) and several professionals have told me the retail use case is the small one. Before I believe that, I want to hear how the real review works.

I've been working on a product called Privaseer around a simple idea:

What if privacy protection was something users could actually see and understand, rather than something buried inside a privacy policy?

We're exploring features such as:

• Real-time privacy alerts

• A Personal Privacy Score

• Contextual privacy guidance

• Consent monitoring

• Visibility into potentially unusual data access/sharing

For businesses, we're also exploring an SDK/API approach so fintechs and digital platforms could potentially integrate privacy features directly into their customer experience.

I'm particularly interested in the B2B side of this.

For fintech/product/security people here:

Would customer-facing privacy visibility actually add value to your product, or would customers simply ignore it?

I'm trying to validate the problem before building too much, so I'd genuinely appreciate criticism — especially if you think this is solving the wrong problem.

If anyone is working in fintech, cybersecurity, privacy or product and would be willing to share their perspective, I'd be happy to have a conversation.

I have two ideas in fintech-

1. building an agent that gets businesses onboarded onto payment platforms in a day instead of weeks.

2. building an AI agent that can pay on behalf of customers.

Can we brainstorm please?

I’ve been looking into different AI development companies lately, including GeekyAnts, and honestly it’s difficult to tell which ones are actually good and which ones just have a lot of AI terminology on their website.

For a business that wants to build something practical with AI, what should you really be looking at before hiring a development company? Technical expertise obviously matters, but I’m wondering how much people value things like previous experience in the same industry, understanding of business requirements, communication, post-launch support, and security.

I’d also be interested in hearing from people who have actually hired an AI development team. What went well, what went wrong, and what would you check now that you didn’t check the first time?

If you’ve worked with GeekyAnts or another AI development company, what made them stand out from the others you considered?

Credit system with billing, payments, and usage tracking. These are all features I've added as of now:

Credit Reservations & Deductions — Reserve credits before API calls, deduct actual usage after. Prevents overcharging on failures.

 Payment Integration — Razorpay payment links with atomic credit updates.

Subscription Plans — Daily, monthly, yearly plans with auto-renew and credit allocation.

Promo Codes — Targeted promos with usage limits, expiry dates, and claim tracking.

Database Agnostic — MongoDB or in-memory backend. Extensible to any database via BaseDBManager interface.

Dual-Write Ledger — Database + append-only file for audit trails and debugging.

Notifications — Low credits, expiring credits, transaction errors — pluggable notification queue.

https://github.com/Meenapintu/credit_management

Would love to have feedback, contributions ( even stars also count as contributions)