Fintech Case Study

Fintech Case Study

Designing for money that doesn't arrive on a schedule

Designing Trust between a Human & an AI Agent - SETU

Self-directed case study · No real users yet, and this doc says so honestly

01

THE SPARK

  • Picked up freelance work on gig platforms

  • Finding clients: easy. Managing money that shows up in random chunks: not

  • Went looking for an app. Found something more uncomfortable than the money problem itself:

  • Opened a money app, gave SMS access

  • 2–3 minutes later: entire financial history reconstructed, from reading text messages

  • Turns out — it's the brand behind an RBI-registered lending company (BNPL, personal loans, FDs)

  • The "budgeting" feature isn't the product. It's the funnel.

The design principle this set for everything after:

The agent's only job is to help the user. Zero hidden monetization pulling the other way.

02

WHY

Why this problem is bigger than it looks?

  • India's gig/freelance workforce: 7.7M → 12M in 4 years (+55%)

  • Now 2%+ of the entire workforce

  • Economic Survey: income volatility directly makes credit access harder for this group

  • Not a budgeting inconvenience — a financial-exclusion problem in disguise

03

WHAT I DID

Why this problem is bigger than it looks?

General Questions

Ques. 1) Who's affected, how many?

Ans. Gig/freelance workers, 7.7M→12M in 4 yrs (+55%), 2%+ of workforce

Ques. 2) What are they doing instead?

Ans. Spreadsheets, mental math, or apps like Axio — a lending funnel dressed as budgeting

Ques. 3) Why do existing tools fail them?

Ans. Flatten irregular income into a fake average, losing the one signal that matters: confidence

Ques. 4) Cost of staying unsolved?

Ans. Economic Survey ties this volatility directly to harder credit access

Ques. 5) Design problem, data problem, or both?

Ans. Both, forecasting logic and how uncertainty gets communicated

Ques. 6) Smallest version that's still genuinely useful?

Ans. Cold start + low confidence + established baseline, manual input only, before edge cases

Specific Questions

Ques. 1) What does "safe to spend" mean with no fixed paycheck?

Ans. see Math section

Ques. 2) How much history = trustworthy forecast?

Ans. Confidence tiers, see Math section

Ques. 3) Warn, restrict, or inform - who decides?

Ans. Inform, not restrict - hard lock was considered and rejected, see below

Ques. 4) What happens when the user disagrees?

Ans. Logged, shapes future forecasts - see Math section, override handling

THE THESIS

If the system shows how confident it is, not just one flat number; people trust and act on it more than a static budget line.

04

THE MATH

  • Safe-to-spend = Trailing avg. income − Fixed expenses − Reserved buffer %

  • Confidence tiers

    • 0–1 mo → wide range, heavily buffered, labeled low confidence

    • 2–3 mo → narrowing range

    • 4+ mo consistent → tight range, high confidence

  • Outlier handling — payment >2x average → partially weighted in over next few cycles, not dumped straight into the baseline

  • Override handling — logged, shapes future forecasts, not silently ignored

05

WALKTHROUGH

Onboarding

  • No SMS permissions

  • 3 steps, everything typed by hand, on purpose

Conversational Dashboard

  • Bank balance & income, blurred by default

  • "Spent so far" / "Safe to save" → visible, the actual point of the product

  • Quick-question chips

Adding income = a small trust ritual

  • Before saving: "Finy will receive: 'I received ₹10,000 on 10 September 2026.'"

  • User sees exactly what the agent gets, in plain words, every time.

06

WHAT I REJECTED

IDEA

Hard Spending Lock

SMS-based auto-import

WHY IT DIED?

Paternalistic treats users as untrustworthy with their own money

The fast path. Ruled out on principle after the Axio discovery, opacity is the exact thing this project argues against

07

HONEST STATUS

  • Self-directed build, not launched

  • No real users tested yet — not dressing that up with a fake number

  • If I tested next: 5–8 real gig workers, watch whether they act on the number during cold-start/low-confidence states, or ignore it like every other app. Override frequency = the real trust signal.

08

WHAT'S NEXT

  • Validate the confidence-tier framing with real users

  • Explore RBI Account Aggregator flow as a consent-based import option, without losing the transparency principle

  • Handle multiple competing High-priority goals, not just one at a time

Redesigning money flow from the ground up

Redesigning money flow from the ground up

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Redesigning money flow from the ground up

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