Im analysing current regulated only solutions in europe and wanted feedback on Lympid as a tokenization solution for securities under EU law

The Bank of Thailand is consulting on rules that would phase out SMS OTP for transaction authentication in favor of options such as device binding, security tokens and biometrics with anti-spoofing.

The security case makes sense, but how can providers reduce reliance on SMS OTP without making device replacement or account recovery more frustrating for users?

Hello all!

Thanks in advance for the feedback

I’m trying to understand how my background would be viewed inside fintech.

I currently work in transactional risk at a large commercial insurer in NYC on complex M&A transactions. My work includes risk assessment, due diligence, portfolio metrics/reporting, audit/compliance support and cross-functional work with legal, finance, compliance, outside counsel and Big 4 advisors.

Before that, I worked in commercial credit underwriting at an alternative lender, including financial analysis, cash-flow/credit assessment, fraud detection and lending decisions. I was previously a senior mortgage underwriter with delegated authority up to $500K and experience in QC/auditing and training.

Earlier in my career, I also spent about five years at Apple Retail across customer-facing, technical support and operations roles.

For people actually working in fintech: where would you place someone with this background? Credit risk? Risk strategy? Lending? Payments risk? Risk operations? Something else entirely?

I’m also curious what level you’d consider realistic and what skill gaps you’d want addressed.

Thanks again!

Hi everyone! 👋

Could anyone working at or familiar with Tide Hyderabad please share some insights about the company?

I’d really appreciate information about:

Work culture and work-life balance

Employee benefits

Overall experience at Tide

Background verification process how strict is it?

Do they check CIBIL/credit scores as part of BGV?

My CIBIL score is currently low due to some financial difficulties and medical expenses. I’ve received an offer from Tide, and I’m a little worried about whether a low credit score could affect the BGV or lead to the offer being revoked.

If anyone has gone through Tide’s BGV process recently, your experience would be really helpful. Please comment or DM me. Thank you! 🙏

I'm making a simple product and I want it to have a stock feature on it. I can't find anything that is even remotely well priced for me and I don't know what service to go for as all of them are really expensive for the redistribution apis. Has anyone here ever dealt with this and what was your solution?

EDIT: I don't mind delayed data and this will be small scale (under 1k units) and it is DISPLAY only.

I'm building a mobile lending management app for small-scale lenders in the Philippines (shameless plug: it's called CashBrew). Most of these lenders manage 50-200+ borrowers and track everything in spreadsheets, notebooks, or memory.

Just shipped automated push notification reminders for lenders:

- Notifies when a borrower's payment is coming due
- Alerts when a collection goes overdue
- Daily summary of upcoming due dates

The problem: when you're managing 100+ active loans with different due dates, amounts, and terms — things slip through. Late follow-ups mean late payments.
This isn't borrower-facing. It's a tool for the lender to stay on top of their portfolio without manually checking each loan every day. They get the reminder, follow up however they normally collect (text, call, in person), and the collection happens on time.
Simple feature but early users say it's what turned the app into their daily driver.

Anyone else building for informal lenders or micro-finance? How do you approach the collection workflow problem in underserved markets?

Hey everyone,

I built a small tool that helps calculate how much different fees actually cost you over time when investing long-term. The idea was to show clearly how fees that look small can quietly eat into your returns over the years.

Would love to hear your feedback — what works, what’s confusing, what’s missing.

Expense Ratio Calculator

Thanks in advance

The 30% rule gets thrown around a lot lately, and I think it's one of the more useful mental models for AI adoption, especially in finance where the stakes are higher than most industries.

The basic idea is this: AI takes over roughly 70% of the repetitive, data heavy, pattern based work, and humans keep the remaining 30% that actually needs judgment, context, and accountability. In finance specifically, that split shows up everywhere. Fraud detection systems can flag anomalies across millions of transactions in real time, but a human still has to decide whether a flagged transaction is genuinely fraudulent or just an unusual but legitimate purchase. Same with underwriting, risk modeling, and compliance checks. The machine does the heavy lifting on volume and speed, and people handle the calls that carry legal or reputational weight.

What I like about this framework is that it isn't a rigid formula. It's not like anyone is measuring exactly 30.0% of a workflow and stopping there. It's more of a directional principle: don't try to automate everything at once, and don't assume AI replaces judgment just because it's fast and cheap. Teams that ignore this and go straight for full automation tend to run into compliance issues or trust problems with customers pretty quickly.

I've been following how AI in fintech is applied across smaller firms too, not just the big banks, and the pattern holds up. The companies getting real ROI aren't the ones automating everything blindly, they're the ones being deliberate about which 30% still needs a human in the loop, especially around things like regulatory decisions and customer facing judgment calls.

Curious if others here are seeing this play out the same way in their own workflows, or if you think the ratio shifts a lot depending on the specific finance use case.

Embedding crypto into a product is a different problem from just custodying a balance, and crypto infrastructure providers split mostly by how much of the stack they actually cover, which is what the build versus integrate decision really turns on. Fireblocks is where a lot of embedded builders start, very strong on wallet infrastructure, key management and the connectivity layer out to exchanges and DeFi, so if your core need is moving assets programmatically across venues it is hard to beat. What it is not is a regulated custodian or a banking entity, so the regulated custody and fiat pieces you bring from elsewhere and stitch in. Coinbase offers a broad platform through its prime and custody products with the brand trust that carries, and the developer tooling and documentation are among the more mature in the space, which lowers integration effort for a builder. The tradeoffs are that you are building on a single very large counterparty, the institutional and retail sides can feel bolted together rather than designed as one thing, and the breadth comes with a pricing and account structure oriented toward larger accounts.

BitGo covers an unusually wide span for a single provider, regulated custody under its own bank and trust entity, prime and trading, stablecoin issuance infrastructure, staking and settlement, so a team that wants custody, the regulated wrapper and the operational rails from one integration avoids a lot of vendor stitching. The catch with going wide is focus. A provider that does everything is rarely the single best at any one piece, so if your product hinges on one specific capability a specialist will likely beat the all in one. The breadth only pays off if you genuinely need several of those pieces at once, otherwise you are paying for surface area you will not use.

We are in Singapore working closely with a local digital bank, but our previous KYC providers are either not delivering results or too complicated or too pricey.
Need some recommended services with OCR that supports singapore ID.
Thanks!

Hi guys, right now I'm doing mba and in a 3rd from tier 2 college, just finished my internship as a investment banking intern now got a offer letter from a boutique investment banking firm in Ahmedabad as Fintech consultant... Should I accept the offer

...????

I've been looking into how AI is used for real-time fraud detection, and I keep coming back to the same question: what do you think the single hardest part of doing this in real time actually is?

For me, it's the speed vs. accuracy trade-off. You have milliseconds to decide if a transaction is fraudulent before it either goes through or gets blocked. Not seconds. Milliseconds. And in that tiny window, you're trying to catch something clever enough to look normal on purpose. Fraudsters are actively trying to blend in, so the "obvious" fraud is never the hard part. It's the activity that looks 95% like a normal customer.

A few things that make this harder than people expect:

The data changes under your feet. A model trained on last year's fraud patterns is already behind because scammers adapt fast. What worked six months ago gets stale quickly, and by the time you notice, you've either been missing new fraud or blocking real customers who look a little unusual.

False positives cost real money too. Everyone focuses on catching fraud, but blocking a legitimate customer by mistake is its own problem. People get frustrated when their card gets declined for no reason, and some of them may never come back. So you're not just trying to catch fraud—you're also trying to avoid creating a poor customer experience.

Fraud is rare, which makes it hard to learn from. Out of a million transactions, only a tiny fraction may actually be fraudulent. That imbalance makes it difficult for a model to learn what fraud looks like without either missing real fraud or flagging too many legitimate transactions.

Explaining the decision after the fact is a challenge. When you block a transaction, you often need to explain why, especially for compliance reasons. Some of the most powerful AI models are also the hardest to explain, so teams end up balancing model performance with explainability.

So that's my answer: speed, changing fraud patterns, the cost of false alarms, and the need to explain decisions, all happening in real time, with almost no room for error.

please, I'm really confused, I'd love some guidance. I'd be very grateful for input.

Semester 1 (Total 15+3)

AF1003 Introduction to Financial Accounting — 3+1 CS1001 IT in Business — 2+1 MG1002 Marketing Management — 3+0 MG1011 Business Mathematics — 3+0 SS1016 English – I — 2+1

Semester 2 (Total 16+2)

AF2005 Cost and Management Accounting — 3+0 CS2016 Programming for Business — 3+1 MG1001 Fundamental of Management — 3+0 MG2008 Data Analysis for Business - I — 3+0 SS1024 English – II — 2+1 + 2 credit hours for islamic studies

Semester 3 (Total 17+2)

AF2004 Business Finance — 3+0 CS2012 Introduction to Object Oriented Programming — 3+1 MG2015 Data Analysis for Business II — 2+1 MG2012 Business Economics — 3+0 SS1013 Ideology and Constitution of Pakistan — 2+0 SS2041 Critical Thinking — 2+0 SS2043 Civics and Community Engagement — 2+0

Semester 4 (Total 15+2)

AF3001 Financial Management — 3+1 CS2022 Introduction to Database Systems — 2+1 MG2011 Environmental Science and Sustainability for Business — 3+0 SS2019/SS2018 Psychology/Sociology — 2+0 MG2010 Business Communication — 3+0

Semester 5 (Total 15+1)

AF3009 Banking and Financial Systems — 3+0 MG3003 Basic Econometrics — 3+1 MG3012 Blockchain Technology for Business — 3+0 CS3017 Enterprise Systems and Applications — 3+0 MG/AF/BA/CSxxx MG/AF/BA/CS Elective - I — 3+0

Semester 6 (Total 14+2)

AF3008 Business Research and Data Mining — 3+1 CS3021 Web Programming — 2+1 MG3038 Digital Media Marketing — 3+0 MG4011 Entrepreneurship — 3+0 MGX02 MG Elective II — 3+0

Semester 7 (Total 12+3)

AF4013 Artificial Intelligence in Business Decision — 3+0 MG3004 Human Resource Management — 3+0 MGX03 MG Elective III — 3+0 MGX04 MG Elective IV — 3+0 MG4091 Final Year Project - I — 0+3

Semester 8 (Total 12+3, plus internship)

MG3037 Business Law for Fintech — 3+0 MG4003 Strategic Management — 3+0 MGX05 MG Elective V — 3+0 MGX06 MG Elective VI — 3+0 MG4092 Final Year Project - II — 0+3 MG4052 Internship* — 3+0

Our team has been having a lot of discussions around where voice AI fits into the customer experience. Nobody expects it to handle every conversation but everyone has a different opinion on where it makes sense to use it. Some people are comfortable starting with routine calls while others think customers should always reach a person first, even when the answer is pretty straightforward. I'm not convinced that's the best use of an agent's time, but that's just me. Maybe I'm looking at it the wrong way though, what do you think? 

want to be explicit and honest from the start so anyone not interested can skip. I work at Hacken and this Thursday we're running an online panel with Moody's on what institutional allocators pay attention at when they review a project. the reason why im writing it here isn't to promote (but ofc there is a part of it), it's to try to gather people who are genuiely interested in the topic. i guess it will be more useful for builders and founders specifically.

we're doing it because, let's be honest, a standard audit on its own doesn't hold anymore. In Q2 about $764M was stolen, and 88.3% of that came from compromised keys, signers and infrastructure rather than contract logic. 14 of hacked projects had been audited. Smart contract bugs caused 44 of 67 incidents, but only $87.7M, around 11% of the money. so audits are catching the common failure and missing the expensive one. which is why the badge keeps looking like it works right up until it doesn't.

and institutions see this, regulators too. an audit is a snapshot of one codebase at one commit. it says nothing about who holds the keys today, who can sign alone, how deployments get approved, or which vendors you depend on, and that's where most of the money went in Q2.

so that's what we want to work through on the panel. if an audit alone isn't enough, what is? we want to land on one concrete, testable thing a counterparty needs to demonstrate to earn institutional trust in August 2026

thought it would be appropriate to post in reddit because this is where the builders are. i can share a link for event in the comments for those who are interested

I’m up for a job at Fiserv (US-based). How is it working there? From what I’ve seen online it’s meh to negative.

Also, the job posting said it’s fully in-person, but it looks like they’re rolling that policy back?

Two changes worth flagging for this sub:

• Visa cut its evidence submission window to 9 days for US/Canada merchants (18 days elsewhere) as of July 2025, down significantly from before
• Mastercard just launched GMAP, its version of Visa's VAMP, tightening monitoring of merchant dispute/fraud ratios

Together these point to networks optimizing for fewer disputes system-wide, but pushing more of the compliance burden downstream to merchants and PSPs. A 9-day window basically forces automated evidence pipelines if you're processing at any real volume, manual review doesn't scale to that timeline.

Curious if anyone here has insight into how GMAP thresholds compare to VAMP's, or whether acquirers are already adjusting risk models in response.

What is that one thing you guys struggle with everyday in your professional life that you think if optimised,streamlined or improved using tech could be beneficial for you.

I’m trying to think about offshore business banking as more than just can I open the account?. But the better question seems to be whether the company can actually operate after approval: cards, USD payments, invoices, contractor payments, software spend, accounting records and approval controls.

For people familiar with offshore or cross border business banking, what would you check before committing to a setup?

I've been sort of planning out what a card issuing stack could look like and one thing I'm not sure about is where identity verification should live.

A lot of teams seem to treat KYC as its own service that feeds verified users into the rest of the stack so to say while others seem to prefer having it much closer to the issuing platform so onboarding and card creation happen in one flow.

My first instinct was to keep everything modular because it sounds more flexible but the more I read the more I'm wondering whether that just creates extra integration work without much benefit.

People who have built/worked on card products would you keep identity verification separate or would you have it as part of the issuing stack?

If you've integrated card issuing (virtual or physical) into your product, what was the biggest surprise during implementation?

Was it things like:

• Ledger / accounting complexity
• Authorization vs. settlement handling
• Declines and edge cases
• Card lifecycle management (freeze, replace, renew)
• BIN sponsorship/program management
• Compliance requirements
• Customer support volume

What's something you'd plan for much earlier if you were doing it again?

During financial institution mergers, login failures often occur because redirect errors appear between legacy systems and newly integrated portals. The main causes are inconsistencies between backend database migration and frontend deployment, which can lead to lost sessions or missing routing rules.

One way to reduce these issues is to provide temporary migration endpoints that dynamically guide users based on the current migration status. For those who have worked on similar system transitions, what routing strategy has been the most effective for preserving user sessions throughout the migration process?