Why Building for Finance is Difficult?

"There is no such thing as a single source of truth in financial data. It's more like a scavenger hunt across dozens of walled gardens." — Tamas Kadar, CEO of SEON Fraud Prevention

7:30 AM. Sophia taps her banking app while waiting for her morning coffee. Her salary has arrived, her EMI is scheduled, and her investment portfolio shows a modest green uptick. She smiles, closes the app, and heads to work.

7:30 AM. Across the city, in a glass-walled office, James stares at his monitor with bloodshot eyes. He's been debugging why Sophia's salary took three attempts to categorize correctly, why her EMI almost got flagged as fraud, and why her mutual fund data is still showing yesterday's NAV. He's been at this since 3 AM.

This is the invisible war behind every seamless financial experience—a daily battle where engineers fight chaos to deliver the illusion of simplicity.

The Illusion: Finance Is Just Data

At first, finance feels like a data problem. And not a very hard one. You think:

• There are numbers.

• You fetch them via an API.

• You do the math.

• You show results.

On paper, a personal finance app sounds easy. A loan decision engine is just a few if-else statements. An invoice scanner just runs OCR.

The First Crack in the Facade

Let me tell you about the Amazon problem.

Last month, a fintech startup's categorization engine broke down completely. Users were furious—their Amazon purchases were scattered across every possible category: some showed as "Shopping," others as "Groceries," and a few even appeared under "Bills & Utilities."

The reason? Here's what their system was trying to decode:

• AMAZON.IN (books)

• AMZN MKTPLACE PMTS (electronics)

• Amazon Pay India (food delivery)

• AMAZON PRIME VIDEO (subscription)

• AMZ*Amazon Fresh (groceries)

• AMAZONPAY*Uber (transportation)

Same company. Dozens of variations. Each transaction looked completely different to the machine, despite being obvious to any human.

Now multiply this by every merchant, every bank, every payment gateway in the ecosystem.

That's just one company. India has over 63 million MSMEs, each with their own way of appearing in transaction data.

Everywhere you look, the data is partial, delayed, out of context, or simply wrong.

The Reality: The Data Is Disorganized

What does "disorganized" really mean in finance?

It means that there is no universal schema. Every institution—banks, bureaus, tax departments, ERPs—uses its data structure, naming conventions, and encodings.

Unlike domains like e-commerce or social media, finance never had a single platform to impose structure. No Stripe or Shopify standard. No OpenGraph or schema.org.

And financial data isn’t just inconsistent—it’s semantically ambiguous. A payment of ₹5,000 can be:

• Rent

• EMI

• Salary advance

• Freelance payment

• Reimbursement

• Bill Settlement

Same amount, the same method, and even the same counterparty. But totally different meanings. To a machine, they're identical. To a human, they determine creditworthiness, cash flow, and tax liability.

That’s why building financial software isn’t just about handling data. It’s about interpreting it correctly in every context, for every user, at every point in time.

The Trust Trap

You'd think official sources would be reliable. Banks, credit bureaus, tax authorities—surely they have clean data?

The Bank Statement That Lied

Last year, a lending startup almost went bankrupt because of this transaction:

Their algorithm flagged it as suspicious—how can someone spend ₹50,000 when they only have ₹45,000? Clearly fraudulent.

But the timestamp was wrong. The bank's system had a 2-hour delay. The actual sequence was:

1. User had ₹95,000

2. Payment of ₹50,000 went through

3. Balance became ₹45,000

4. Bank reported it with the wrong timestamp

The user's loan got rejected. They switched to a competitor. The startup lost a good customer over bad timestamp data.

The Credit Bureau Ghost

Credit bureaus maintain records of loans that were "closed" years ago but still show as active. Why? Because the lending bank's system marked them as "settled" instead of "closed"—two different statuses that mean the same thing to humans but create different credit profiles for machines.

Result: Good borrowers get rejected, and bad borrowers slip through.

Quick Question for You:

Imagine your banking app shows a ₹5,000 transaction you don't recognize, and it's uncategorized.

Who is the first entity you'd likely feel frustrated with?

• A. The bank itself.

• B. The merchant (e.g., "AMZN-MKTPL").

• C. The finance app you're using.

• D. Your own memory!

The Performance Under Pressure

But here's the cruelest part: the better you handle this chaos, the more invisible your work becomes.

The 3 AM Success Story

Remember James from our opening? Here's what his night looked like:

3:17 AM: The user complains that their salary isn't showing up. James discovers their company changed payroll providers, and the new system sends salary with description "SAL-CREDIT-EMP001" instead of the expected "SALARY PAYMENT."

3:42 AM: He writes a new pattern matcher, deploys it, and the salary appears correctly.

4:15 AM: Another user reports their mutual fund investment is showing wrong returns. The AMC's API is returning last Friday's NAV instead of current values due to a weekend processing glitch.

4:33 AM: He implements a fallback to fetch NAV from a secondary source, with data validation.

5:20 AM: A bulk of transactions get miscategorized because a popular food delivery app changed its merchant name from "Swiggy" to "Bundl Technologies."

5:45 AM: New merchant mapping added, historical transactions re-categorized.

6:30 AM: All systems are green. Users wake up to perfect data.

7:30 AM: Sophia checks her app, and everything works perfectly. She never knows about Rohit's night.

The paradox: The better James does his job, the more it looks like there is no job to do at all.

This Is Why Fintech Teams Burn Out

Building financial software isn’t just a technical challenge—it’s an emotional one.

You spend weeks building something elegant. A seamless loan journey. A beautiful savings graph. A clear tax summary.

Then you test it in the real world, and it crumbles under:

• A broken PDF parser.

• A bank that returns the wrong timestamp.

• A bureau that crashes mid-query.

You pivot. Patch. Write regex rules. Build edge-case validators. Add disclaimers. Delay launches.

After a while, your product becomes not a vision—but a fortress. A fortress against bad data.

It’s no wonder so many fintech products look generic. Builders stop dreaming. They start firefighting.

40–60% of time in data projects is spent just cleaning the data.

McKinsey, 2021

Why We Keep Building

Despite all this chaos, despite the sleepless nights and endless edge cases, we keep building. Why?

Because when it works—even for a moment—it feels like magic.

The Magic Moments

Sophia’s Story Continues: Six months later, Sophia gets a notification: "You're spending 23% more on food delivery this month. Want to see nearby grocery stores?" She taps yes, discovers a store 2 minutes from her office, and saves ₹3,000 that month.

The Small Business Owner: Lucas runs a textile shop. His fintech app automatically categorizes his supplier payments, calculates GST liabilities, and reminds him of upcoming compliance deadlines. What used to take his accountant 3 days now happens automatically.

The First-Time Investor: Emma, 24, gets her first salary. Her investment app analyzes her spending, suggests a SIP amount, and sets up automatic investments. She builds wealth without thinking about it.

The Loan Approval: William needs emergency funds for his father's surgery. Traditional banks would take weeks. A fintech app analyzes his digital footprint—salary credits, bill payments, investment patterns—and approves ₹2 lakhs in 3 minutes.

These aren't just product features. They're moments of economic empowerment.

The Future We're Building

Every cleaned dataset, every edge case handled, every fallback system built—it all adds up to something bigger.

We're not just building apps. We're building economic infrastructure. We're making the opaque transparent, the complex simple, the exclusive accessible.

The Vision

• Universal Financial Inclusion: When data works perfectly, everyone gets access to financial services

• Real-Time Decision Making: Instant loans, investments, and financial planning

• Predictive Financial Health: Systems that prevent financial problems before they happen

• Automated Compliance: Taxes, regulations, and reporting are handled seamlessly

Next time you effortlessly check your balance, track your spending, or apply for a loan through an app, take a moment. Remember the engineers, data scientists, and product builders meticulously taming the wild beast of financial data, turning confusion into clarity, one complex puzzle piece at a time.

Their work, often thankless and invisible, is the bedrock of modern financial empowerment. It’s why FinTech isn't just about innovation; it's about an ongoing commitment to diligence, accuracy, and ultimately, trust. Supporting these efforts means supporting a more transparent and accessible financial world for us all.