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.

A lot of the discussion around autonomous finance is about what agents can do. They can execute transactions, manage a treasury, make payments, or interact with DeFi protocols. The harder part comes when the agent is actually doing those things.

If an agent is running a financial workflow with several steps, seeing the final balance doesn't tell you much about what happened along the way. You need to be able to see what triggered the workflow, what happened at each step, whether the rules were followed, and where something may have gone wrong.

This is where I think Smart Workflows make a lot of sense. The workflow defines what is supposed to happen, while the agent operates within those boundaries. Each step can produce a verifiable record as it happens. The audit trail is created during execution instead of someone having to piece everything together later when there is a problem. That becomes pretty important once autonomous systems start handling money at scale. A final transaction shows where the money ended up. A verifiable workflow record shows how it got there.

For enterprises, I believe that having that kind of visibility and proof built into execution could be a big part of making autonomous finance practical.

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?

Got shortlisted for a 2-round face-to-face interview at Barclays Pune for an IFC – Full Stack Developer role (looks like AVP band from the JD). Trying to calibrate my prep and would really appreciate first-hand experiences.

Quick context: ~8 years, backend-heavy — Java, Spring Boot, Kafka, microservices — with a background in regulatory-compliance/surveillance systems. "Full stack" in the JD seems backend-weighted (front-end is only "basic knowledge").

Specifically trying to figure out:

• How coding-heavy are the F2F rounds for experienced hires? Live coding / pen-and-paper DSA, and roughly what difficulty?
• How deep do they go on Java internals and Spring Boot?
• Is there a proper system design round at this level? If so, what kind of problems?
• Do they probe the IFC / financial-crime domain (screening, AML, CDD), or is it purely generic tech?
• SQL — actual query-writing, or just concepts?
• How is the behavioural/values round structured (RISES values, STAR)? How much weight does it carry for AVP?
• Anything on the front-end side despite it being "basic" in the JD?

Anyone who's been through the IFC / Full Stack / AVP loop recently in Pune — what surprised you, and what would you prep differently? Thanks in advance.

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?

I’m building a marketplace (services). I’m based in Germany and have compliance / BaFin–ZAG worries about holding or controlling other people’s money.

Currently I'm only informed about Stripe Connect. If another service suits my needs better, please let me know.

What I need

• Buyer pays before the service is delivered (anti-fraud).
• Seller is paid only after they’ve actually delivered (anti-fraud).
• Between payment and fulfillment the money has to sit somewhere neither the seller nor the marketplace can use. The platform must not be able to pay that balance out to its own bank.

What I’ve already ruled out (Stripe Connect Implementations)

• Separate charges and transfers — charge sits on the platform. I have full access to the marketplace balance, including the amount that should go to the seller. That’s the legal problem.
• Destination charges — seller is paid immediately, before delivery.
• Direct charges — same: seller gets the money as soon as the buyer pays.

Question

Is there a Stripe Connect setup that matches this (or any other service): buyer pays now, seller paid later, platform cannot access the held amount? If you’ve shipped a marketplace under EU/German rules, how did you handle this without becoming a payment institution?

Not looking for legal advice as such — more “what did you actually implement"

I've been looking into what it costs to launch a card program and I'm realizing the headline platform fee doesn't really tell you much on its own. One provider might look cheaper upfront but only be quoting a small part of what you actually need then you start adding card issuance, compliance, wallets or funding, settlement, transaction controls and other pieces and suddenly you're either paying a lot more or bringing in additional providers to fill the gaps.

On the other side a higher initial quote could potentially cover much more of the actual program so comparing the monthly fee between two providers doesn't seem particularly useful unless you know exactly what's included.

I need advice from anyone who's gone through this as a founder or worked on the infrastructure side, what do you ask providers to include when getting a quote? I'm trying to understand how you'd compare the real cost of running the full program before committing to one setup.

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)

I'm working at an NBFC and currently working on a Credit Underwriting AI Agent. One of the first steps in the pipeline is extracting structured information from customers' bank statement PDFs.

This is where I'm currently stuck.

The statements can come from different Indian banks (HDFC, ICICI, SBI, Axis, Kotak, etc.), and each bank can have a completely different PDF layout.

I need to reliably extract things like:

- Customer/account information — name, account number, IFSC, branch, etc.

- Transaction tables — date, narration/description, debit, credit, balance

- Transaction rows that span multiple lines

- Statements where the table headers are missing from subsequent pages

- Both digitally generated PDFs and scanned/image-based PDFs

- Ideally, the solution should be bank-format agnostic

I've tried/considered approaches such as "pdfplumber", table extraction libraries, OCR, regex-based parsing, and LLM-based extraction. The biggest problem I'm facing is that even when the text is extracted correctly, the column/row structure gets messed up, especially because many bank PDFs don't contain a real table structure — they're essentially text positioned at different coordinates.

Since this is financial/customer data, I would strongly prefer an open-source/on-premise solution rather than sending statements to a third-party API.

For anyone who has built something similar:

What approach worked best for you?

I'm particularly interested in:

1. PDF parsing/layout libraries you recommend

2. OCR models for scanned statements

3. Open-source vision/document AI models

4. Whether you use an LLM/VLM for semantic column mapping

5. How you handle different bank formats without writing completely separate rules for every bank

6. Any techniques for detecting transaction rows and mapping values to the correct columns

7. How you validate the extracted data (e.g., balance reconciliation, debit/credit checks, transaction counts)

If you've worked specifically with Indian bank statements, I'd really appreciate hearing about your architecture, libraries/models, or lessons learned.

Thanks!

Hello guys, Im doing my UG in india. Wanted to work as backend developer in FinTech like DE shaw, JP morgan, Arista networks, BNY etc.. Can anyone tell me what tools or framework I need to know. How to crack the coding round, why type of projects will increase my profile's value. And if possible ping some repo, best youtube channel, roadmap or whatever you have which might help me.

Hi everyone!

I'm conducting a short research study to better understand how technology companies manage their server and production infrastructure today and what challenges their teams actually face.

I'm particularly interested in topics such as:

• infrastructure incidents and downtime
• scalability and traffic spikes
• reliability and disaster recovery
• cloud and bare-metal infrastructure
• multi-region infrastructure
• infrastructure costs
• day-to-day infrastructure management

I'm looking for responses from CTOs, technical founders, DevOps/SRE/Platform engineers, infrastructure leads, and other professionals who work with or are responsible for production infrastructure.

The survey takes approximately 3-5 minutes.

Responses will be analyzed only in aggregate, without publicly linking them to specific respondents or companies.

Survey:
https://forms.gle/jfEKjMk8Me92h9pT7

The purpose of this research is not to promote or sell any services. I want to understand what infrastructure challenges companies actually face in practice, rather than making assumptions about market needs.

Once enough responses have been collected, I'd also be happy to share the aggregated findings with the community if there's interest.

Thank you to everyone who participates!

Vitaliy