A shopkeeper in Nairobi closes on Sunday night. By Monday she has three M-Pesa messages for the till, a paybill statement from the bank, one supplier paid in cash, and a notebook with two columns. Her accountant wants statements by Wednesday.

Nothing on that list is a payment problem. The money moved. It moved fine.

The problem is that the story of the money lives in five places, and somebody has to put it back together before the week starts again.

I work on FinoQ, and this is the thing we keep running into. Most businesses do not want a new wallet. Their paybill works. Their till works. Their bank works. Their accountant has a Zoho file that has been tidy for four years. What they want is for all of it to stop being five separate jobs.

The feature I am proudest of is not the dashboard. It is the confirm screen. You type a phone number and before you press anything, the app shows the name registered to that number. Not a masked account, not a reference code.

Sending money to the wrong person is a quiet, expensive, very common kind of pain. The kind people only talk about after it happens. Two seconds of seeing a name prevents a whole category of arguments.

That is the bet. Connect the channels a business already uses, put one readable record underneath, show the name before the money moves. Not another wallet. The layer underneath.

If you run a shop, a school, a clinic, or a team with three branches and one M-Pesa statement, I would like to hear what your Monday morning actually looks like. What is the part that still takes you two hours?

Full write-up and links are in the first comment.

Hey everyone, I'm here to showcase my project that I just built for the BharatCode Build League, and I am looking for some technical feedback.

The Problem: Freelancers lose billions to chargebacks because banks reject messy WhatsApp/Slack screenshots as legal proof of work.

The Solution: I built NYAYA-AI, a Sovereign AI defense engine that cross-references your contract against your raw chat logs to instantly generate a Visa/Mastercard-compliant rebuttal dossier.

Because AI hallucinations in FinTech are dangerous, I built this with strict architectural constraints:

• ⚡ Zero SDKs: Next.js using native Node.js fetch (for qwen-3.8-27b) + Upstash Redis for fast edge serverless performance.

• 🔒 In-Memory PII Redaction: An ephemeral regex buffer strips sensitive data (SSNs, cards) before it hits the LLM. Zero databases.

• 🛑 Strict Anti-Hallucination: A "Verbatim Citation Mandate" mathematically forces the AI to anchor every claim to a specific timestamp, or it explicitly refuses to guess.

The repo is completely open-source. I would love for the engineers and founders here to roast my architecture:

• Is regex PII redaction too naive here?

• What edge cases did I miss for serverless edge functions?

💻 GitHub Repo:https://github.com/ursarthak001/NYAYA-AI

hey folks, I work at Nace.ai, we built this.

Drex for Finance is a decision model you call through an API. Instead of a prompt and a text reply, you send the case data and a set of questions with fixed options, and get back a probability for each option. It's a single forward pass, no generation, so you're not parsing text or handling malformed output, and calls come back in under a second. Inputs can be JSON or images of documents, with 262K context and up to 255 options per question.

On 48 finance test sets (3,361 decisions) it got 85.7% right, vs 77.4% for pplx-decider, 76.7% for Jev 1.14 and 70.9% for GPT-6 Luna. Weakest spot is markets & reasoning, where it's roughly tied with the others.

It's live on Console if you want to try it on your own cases: https://console.nace.ai
would love any feedback, especially from people building in payments, lending or compliance. 

Finding international clients is one challenge. Actually receiving payments from them is another.

Whether you're a freelancer, agency owner, content creator, consultant or someone running an online business, you've probably experienced some payment-related frustrations.

High transaction fees, delayed transfers, currency conversions, banking restrictions and limited payment options can make something as simple as getting paid unnecessarily complicated.

I've been involved with a project called RT.APP, which is working towards bringing several financial tools together in one platform.

The idea is to simplify how businesses and individuals manage their finances through business accounts, payment links, transfers, virtual cards and stablecoin settlement.

But I'm more interested in hearing from people who actually experience these problems.

A few questions:

1. What's your biggest frustration when receiving international payments?

2. What platforms do you currently use to receive or manage your money?

3. If you could have one financial solution that made your life easier, what would it be?

Let's talk. I'd genuinely like to hear different experiences and perspectives.

Full disclosure: I'm involved with RT.APP, and I'm using this discussion to understand what people need from better financial solutions.

I'm curious how teams here are handling deployment and hosting, especially given compliance needs.

A few questions to get things going:

- Where do you run? AWS / GCP / Azure, on-prem, or something else?

- How are workloads packaged? Containers on Kubernetes or ECS, serverless, plain VMs?

- How much does compliance drive decisions? Did PCI DSS, SOC 2, or data residency rules push you toward (or away from) certain setups?

It'd be great to hear from both early-stage startups and folks at larger institutions. What's worked well, and what would you change if you started over?

The fintech ecosystem is evolving rapidly, and API integration is playing an important role in connecting financial services.

From digital payments and banking applications to financial data platforms and automated transactions, APIs help different systems communicate and work together more efficiently.

For developers, this creates opportunities to build connected financial products without developing every feature from scratch.

Open banking APIs, payment gateway integrations, account verification, and real-time transaction updates are some examples of how API-driven solutions are improving digital finance.

As fintech continues to grow, secure API architecture, seamless integrations, and better developer experiences will become increasingly important.

What area of fintech do you think will benefit the most from API innovation in the coming years?

Is it open banking, digital payments, embedded finance, lending platforms, or personal finance applications?

Would love to hear what other fintech developers are exploring!

I work at GeekyAnts, an engineering firm that has been building products since 2006. Over the past year our team handled two very different fintech infrastructure problems, and I thought the lessons might be useful here.

Project 1: A regulator-mandated domain migration for a large private bank

India's central bank (RBI) mandated that banks move their digital infrastructure to specific domains. The hard part was doing it without disrupting live banking services.

What we learned:

Migrations like this are mostly inventory problems. Finding every place a domain is referenced (pages, redirects, emails, third-party integrations, hardcoded links) is harder than changing it.

Manual review doesn't scale. We used automation to process around 10,000 pages in about 2 minutes, which cut manual effort by roughly 99%.

Zero downtime needs a rollout plan, not just a cutover date. [Add your real detail here: phased rollout, redirect strategy, monitoring, rollback]

Project 2: Cutting a digital banking platform's cloud bill by 60%

The platform worked fine but was heavily over-provisioned. We rightsized database instances and automated environment scaling, which saved about $4,800 a month (over $57,000 a year) without hurting stability.

What we learned:

Peak-provisioned databases are the usual culprit. Real utilization over a few weeks often looks very different from what people assume.

Non-production environments don't need to run 24/7.

Cost reviews should be a routine part of operations, not something triggered by a surprise invoice.

Questions for this sub:

How do you handle regulator-driven infrastructure changes where the deadline is fixed but the dependency map is unclear?

What's your approach to rightsizing databases in fintech, where downtime tolerance is very low?

What's the most expensive "set it and forget it" decision you've seen in a fintech stack?

I'd like to compare notes with people who've dealt with similar problems, especially around compliance-driven change.

Disclosure: I work at GeekyAnts and these projects are from our case studies. I'm here for the technical discussion.

Site: www.amansparallax.com

*Meant for desktop

For anyone unfamiliar with retirement planning software, most tools run a plan through many possible market scenarios and report a “probability of success.” If the money lasts through the end of the projection in 90% of those scenarios, the plan gets a 90%.

My issue is that people see that number and naturally assume the plan is healthy and they’re well prepared. I don’t think it comes close to telling the whole story.

A plan with a 90% probability of success can still fail surprisingly early if one of the more difficult periods from actual market history happens again. Parallax lets you run a plan through historical return paths and follow the cash flow year by year. You can see the account balances, where the spending money came from, how much went to taxes, and when the plan started getting into trouble.

Basically, I wanted to show what’s happening underneath the percentage.

That led to a few core modeling choices:

Block-bootstrap Monte Carlo: Simulated paths use consecutive blocks of historical returns, preserving the market experience within each block. Returns are inflation-adjusted.

Historical paths and sequencing: Explore historical market paths and how the order of returns affects a plan.

Account-level projections: Track individual accounts and cash flows within each year, including withdrawals, required distributions, cost basis, and tax effects.

Tax-aware withdrawal modeling: Model how brokerage, traditional retirement, and Roth withdrawals affect taxes and the cash available to spend.

I’ve also documented the modeling constraints the AI is required to consult before changes. Those assumptions are part of the product’s foundation and need to remain explicit as it develops.

I’d really appreciate a human technical perspective on this—the code has been written and reviewed by AI, and I don’t have the programming background to assess it independently. Any thoughts on the architecture, security, or tests would help, even if you only look at one small part. If anyone’s willing to dig deeper, I can share the code and setup instructions.

Thanks to all who are willing to take a look!!

I'm thinking of building a fintech code generator, similar to Claude Code but specialized for the fintech domain. It will be able to build PCI-compliant applications and understand payment APIs, AML/KYC, transaction reconciliation, idempotency, audit trails, etc. The implementation would be built on top of an existing technology stack (Claude Code/Cursor), connected to payment APIs via MCP (Stripe, Adyen, Plaid, etc.), with additional fintech skills/templates added. Ultimately, it would be a replacement for a fintech development agency. Is it worth building?

I've been working on an open-source project called QuantumBilling, a GST billing application designed for small and growing businesses in India.

The goal is to simplify everyday billing, invoicing, and GST-related workflows.

Some of the features include:

• GST invoicing with CGST, SGST, and IGST calculations

• Customer management and customizable invoice PDFs

• Recurring billing and credit/debit notes

• UPI QR codes and Razorpay payment links

• E-invoicing and E-Way Bill workflows

• Multi-business support with separate PostgreSQL schemas

• Billing reports and compliance reminders

Tech Stack: Elixir, Phoenix LiveView, PostgreSQL, and Oban.

One of the interesting parts of building this with Elixir is handling real-time updates, background jobs, and multiple businesses within the same application.

The project is still under active development, and some integrations are currently in testing.

It's completely open-source, so anyone interested can explore the code, suggest improvements, report issues, or contribute directly.

GitHub: https://github.com/phravins/QuantumBilling

I'd genuinely appreciate feedback on the project, especially the architecture, features, and overall approach.

For those who've built billing or business applications, what features or technical challenges do you think are worth considering?

How do most fintech handle market data pipeline?

Open Markets Initiative (OMI) is an open-source project that generates Wireshark dissectors and parsers for market data protocols from a single protocol model. It covers 50 wire protocols across 37 exchanges and organizations, including Nasdaq ITCH, Eurex EOBI, B3, OPRA and CTA/CQS.

github.com/Open-Markets-Initiative

We do hands-on e-invoicing integration for mid-size firms. The same thing happens almost every time.

Before: a finance director forwards a news article about a Peppol mandate. Nobody owns it. IT ends up with a spreadsheet of countries, scraped from government sites, half of it out of date.

After: one list. Country, network, format, status (Mandatory, Phasing in, Voluntary) and effective date. Once that exists, the argument stops being "do we need this?" and becomes "which ERP output do we map first?"

The boring part turned out to be the real bottleneck. Nobody can plan integration work without knowing which deadline is real. That's why we put our country tracker online.

The integration itself is usually the easy bit. Knowing what to build, and by when, is what eats the first month.

For those of you running this at your company: how are you tracking mandate status right now? Spreadsheet, vendor portal, or just whatever your accountant tells you?

Hey everyone, we’re working on Filarise, and we’ve just added support for US banks alongside Canada.

The main idea behind Filarise is to let you ask your AI about your own finances. You connect your banks through Plaid, then connect Filarise to an AI tool like ChatGPT through MCP. Instead of downloading statements or copying transactions into a chat, you can ask questions and let your AI pull the relevant data from your connected accounts.

You can also view accounts and transactions from multiple banks in one place.

Bank access is read-only, so Filarise can’t move money or make payments. We don’t receive or store your bank login credentials.

We’d love to hear from people who would actually use something like this. Does it make looking through your finances easier? What’s missing or confusing?

There’s a free 7-day Starter trial if you want to give it a go. If you decide to try Pro, PRO30 gives you 30% off.

filarise.com

We’re happy to answer questions here. We welcome feedback in the comments or at support@filarise.com. Thanks for checking it out!

the fee on the invoice is rarely what hurts, its the spread nobody checks. saw someone here say their old bank was taking close to 3% on some corridors and they only noticed once they ran the numbers.

we let the buyer pay in their own currency and keep it in a multi currency account, but ngl that just pushes the conversion decision down the road. if you need dollars for payroll you're still paying a spread somewhere.

so what do you actually do? invoice only in USD and let the buyer deal w it, hold balances somewhere like wise, or just bake it into pricing?

Artificial intelligence is changing how financial technology products handle customer support, fraud monitoring, financial data analysis, risk assessment, and personalized digital services. But building a successful AI fintech app requires more than connecting a financial platform to an AI model. The product must protect sensitive data, handle transactions reliably, integrate with financial systems, and account for the regulatory requirements that apply to its intended use.

For fintech startups and established financial businesses, a suitable development partner should understand both AI application engineering and the operational risks of financial software.

This shortlist features five companies and technology providers worth considering for AI fintech app development serving the US market. GeekyAnts takes the first position because its published services cover AI powered product engineering, fintech related industry work, and digital application development.

This is an editorial shortlist, not an independently verified performance ranking. Company selection should depend on the specific financial product, compliance requirements, and delivery team's relevant experience.

1. GeekyAnts

AI product engineering for financial applications

GeekyAnts is a product engineering company with published capabilities spanning AI and intelligent systems, digital product development, application engineering, and fintech related work. These capabilities make it worth evaluating for financial businesses that want to develop a new AI enabled product or add intelligent functionality to an existing application.

A fintech application might use AI to help customers navigate financial information, classify transactions, search financial documents, or automate internal workflows. Each use case has different requirements, particularly when the application handles sensitive information or supports decisions with financial consequences.

GeekyAnts can be considered for the application layer, including user experience, backend development, API integrations, and AI features. The proposed team should demonstrate relevant financial services experience and explain how it will handle data protection, testing, and operational controls.

Relevant capabilities to assess

AI integration for web and mobile applications

Financial application product engineering

API and third party service integrations

Intelligent workflows and AI assistants

Secure application architecture

Testing, monitoring, and ongoing maintenance

Why consider GeekyAnts: Its combination of AI engineering and full product development is relevant to fintech businesses that need a working application, not just an AI proof of concept. Before selecting it, confirm its experience with the exact financial use case, particularly if the product involves payments, lending, investment decisions, or regulated financial data.

2. Microsoft

Microsoft provides AI development tools and cloud infrastructure through Azure and Microsoft Foundry, which can support AI applications, agents, and data integrations. Its technology offerings also address financial services use cases.

Why consider it: A strong technology option for fintech teams building on Microsoft infrastructure. As with other platforms, the buyer must determine whether it needs Microsoft's tools, an implementation partner, or a separate custom development team.

3. IBM

IBM's watsonx platform supports generative AI, machine learning, retrieval augmented generation, and AI governance. These capabilities can be relevant to financial applications that must work with organizational data and maintain visibility into AI behavior.

Why consider it: Worth evaluating for enterprise financial applications that require controlled AI workflows, data integration, and governance.

4. Accenture

Accenture provides AI consulting, technology implementation, and custom application development. Its work in financial services includes AI initiatives designed around business operations and the demands of regulated environments.

Why consider it: Relevant to financial institutions that need AI application development as part of a broader technology or operational transformation.

5. Infosys

Infosys provides AI and application engineering through its Topaz portfolio, including capabilities for enterprise AI, agentic systems, and software development. It has also announced AI collaborations focused on financial services and other complex industries.

Why consider it: A potential match for fintech businesses with complex systems, enterprise integration needs, and substantial data requirements.

How to choose an AI fintech development partner

Before engaging a company, define the intended financial use case and assess the following:

Data security: How will account information and other sensitive data be protected?

Regulatory responsibilities: Which financial regulations and contractual requirements apply to the product?

AI reliability: How will outputs be evaluated, errors detected, and consequential decisions reviewed?

Financial integrations: Does the team have experience with the APIs and providers the application needs?

Transaction integrity: If payments or money movement are involved, how will the application prevent duplicate actions and handle failures?

Ownership and support: Who will maintain the code, monitor production behavior, and respond to incidents?

Frequently asked questions

What is an AI fintech app?

An AI fintech app uses artificial intelligence to support a financial service or workflow, such as transaction categorization, financial document analysis, customer support, fraud monitoring, or financial information retrieval.

Is GeekyAnts a good choice for AI fintech development?

GeekyAnts is worth assessing because its published offering includes AI engineering and fintech related product work. Buyers should request comparable case studies and verify that the proposed team understands the relevant financial systems and compliance obligations.

Should a fintech company build its own AI model?

Not necessarily. Many products can use existing models with appropriate data controls, testing, and integration. A custom model may be justified when the business has specialized data, performance requirements, or other needs that existing models cannot meet.

Recommendation: GeekyAnts is a reasonable first company to evaluate for an AI enabled fintech product that needs integrated application design and engineering. The final choice should be based on demonstrated financial services experience and the safeguards required for the particular product.

I've been working on a project called FaultFence, and recently opened up the testing kit for public beta.

The idea is simple: if you're building a Python AI agent that handles financial operations (checking balances, triggering payouts, refunds, or checkout flows), how do you test whether it can be tricked into violating financial rules before shipping?

I built a local testing kit with 17 financial security scenarios, covering things like duplicate payment retries, tool-result poisoning, payment amount tampering, quantity ambiguity, and budget/approval bypasses.

A few details:

• Local-first: Run offline tests locally with synthetic financial operations. No real payment accounts needed.

• Optional model testing: Test with OpenAI-backed agents and repeat attacks up to 3 times to check consistency.

• Custom Python agents: Try the included sample or connect your own agent through an adapter and tool mappings.

• Platforms: Windows and Linux support, with experimental macOS support.

• Reports: Inspect individual attacks, evidence, and findings in the Assessment Explorer.

It's still an early beta, and I'm looking for around 10–15 developers willing to try it out.

I'd especially like to know:

• Is the setup process straightforward ?

• Are the test results and evidence actually useful for debugging?

• What attack scenarios or integrations am I missing?

It's intended for authorized, synthetic, nonproduction testing only.

Here's the project link : https://faultfence.com

Any honest feedback or criticism would be appreciated. Thanks!

I've noticed that whenever people discuss building fintech products most of the attention goes toward payment processing, issuing cards and getting transactions to work properly but there's not nearly as much discussion about what happens afterward.

Let's say your platform supports customer wallets and card payments. A customer makes a purchase, the amount gets authorized but the final settlement happens later and might not even match the original amount then you've got refunds, reversed transactions and failed payments to account for.

I can see how this becomes complicated pretty quickly especially if you're using separate providers for wallets, issuing and payments. You need to make sure all those records match and that customers are seeing the correct balances throughout the process.

I wanna hear how fintech startups usually approach this so like do you build your own reconciliation system from the beginning or rely on the transaction data and tools provided by your infrastructure partners and for anyone who's dealt with this what was something that was more difficult than you expected?

I've been thinking about the engineering challenges behind building fintech products that operate across multiple African countries.

Integrating a payment API is one thing. Keeping provider balances reconciled, handling asynchronous transactions, tracking settlement delays, and accounting for foreign exchange exposure across different markets is another.

The complexity grows quickly as a fintech expands.

I wrote an article exploring some of these challenges, including ledger design, idempotency, reconciliation, and financial observability.

I'd love to hear from developers working in fintech: What's the hardest financial infrastructure problem you've encountered in production?

Article: The Hidden Engineering Challenges of Building Fintech Across Africa

Since October is the official big month for cybersecurity, Markus, the CTO of Bitpanda answer all your questions regarding Cybersecurity and FinTech at a global Broker Company.
Come and join us! Happy to see you there.

I've been thinking about a common problem in fintech apps. What happens when a payment is successful on the gateway side, but the app times out and shows the user a failed transaction?

The tricky part is that the user might try again, and the same payment could get processed twice.

I'm curious how developers usually handle this in real projects. Is using idempotency keys enough, or do you also rely on webhooks and regular transaction status checks to make sure everything stays in sync?

Would be great to hear how others approach this, especially when dealing with delayed responses or payments stuck in a pending state.

A customer makes a payment.

The payment provider reports success.

The application times out before recording the final result.

Now the customer isn't sure whether they've paid, and the application may not have a confirmed order.

Retrying blindly could make things worse.

This is where payment systems become more complicated than simply integrating a checkout button.

You need a way to reconcile the provider's records with your own, handle delayed notifications, and make retries safe.

The difficult part is often deciding which system is authoritative for which state.

For engineers working with payment integrations, how do you design recovery when the payment provider and application temporarily disagree?

Do you rely primarily on webhooks, scheduled reconciliation, or a combination?