Millions of small entrepreneurs in India are locked out of formal credit. P2P lending promised to reach them — but the platforms kept stalling, because neither side could trust what they couldn’t see. I designed a trust-first system that makes risk legible to lenders and lending legible to borrowers.
The lender dashboard — every opportunity risk-scored, portfolio health visible at a glance.
The borrowers were there. The lenders were there. The trust between them wasn’t.
India’s microlending ecosystem serves millions of small entrepreneurs shut out of traditional banking — no collateral, no formal credit history, no documentation. P2P platforms promised to reach them with faster capital and better returns. But for users in Tier 2 and Tier 3 cities, the experience collapsed under complexity and uncertainty, and the funnel leaked at exactly the moments that required confidence.
Research across lenders and borrowers, plus competitive teardowns of LendBox, LenDenClub and LiquiLoans, kept circling the same root cause. Both sides of the marketplace suffered from one identical thing.
This was the unlock. Lenders already drowned in partial dashboards and fragmented profiles. Piling on more raw data made decisions harder, not easier. What they needed was interpretation: a risk grade, a confidence level, a clear default signal — the data translated into a decision they could trust.
Underserved borrowers weren’t scared of loans. They were scared of rejection and of repayment terms they couldn’t see clearly. The answer wasn’t to hide complexity — it was to make the consequences of each choice visible and reversible before they committed.
One structural decision drove the whole architecture: separate the financial data from its risk interpretation. Raw numbers in one place, the meaning of those numbers in another. That separation is what let us add confidence cues without burying lenders in more dashboards.
Centralized borrower data with business context, income patterns and repayment behaviour — so a thin credit file becomes a readable story instead of a blank space.
Alternative-data scoring, progressive credit grades and early default warnings — each shown with a confidence level and a plain-language reason, never a black-box number.
Repayment scenario forecasting and a risk-return tradeoff visualiser. Both sides see the consequences of a choice before they commit to it.
Portfolio health and repayment timelines surface risk early, turning lenders from anxious spectators into informed managers of their own exposure.
| Before | After |
|---|---|
| Manual evaluation of partial profiles | Risk-scored opportunities, ranked |
| High uncertainty at decision time | Clear grade, confidence and reasoning |
| Defaults discovered late | Proactive early-warning monitoring |
| Fragmented, anxious analysis | One legible view of portfolio health |
What the work moved
Targets the design is built to drive
I shifted the goal from “more loans” to “better loans.” Inclusion only works if lenders keep showing up.
This is the central tension of inclusive lending. Push inclusion too hard and you fund risky borrowers, defaults rise, and lenders flee — which ends inclusion for everyone. Tighten risk too hard and you rebuild the same exclusionary wall that traditional banks already are. The platform’s instinct was to chase loan volume. Research said that was the path to a default spiral.
So I made the call to design for better loans, not just more of them — using explainable risk signals and alternative data to bring thin-file borrowers in responsibly, rather than indiscriminately. That reframed inclusion as a sustainable system instead of a growth-at-all-costs bet. The win wasn’t maximizing one side; it was keeping both sides of the marketplace willing to participate.
Introduce financial-literacy tooling earlier, so borrowers build understanding before they hit the application, not during it. And invest in regional personalization sooner — a single flow can’t serve the linguistic and literacy range of Tier 2 and Tier 3 India, and the SUS gap hinted that one size was already straining.
Client — Early-stage P2P microlending startup
Role — UX Design Consultant: research, strategy, system design
Team — Satyajit Roy (UX Consultant) · a junior designer · a UI & interaction designer
Timeline — ~10 weeks: 1 mo research · 2 wk ideation · wireframes, design system, testing & final UI
What I owned — Research strategy, persona & mental-model definition, the risk-and-trust UX framework, AI prompting for risk-assessment workflows, and the reframe from “more loans” to “better loans.”