Prove your lending AI
is fair — before
it reaches production.
fair lend. is a GenAI-powered, human-in-the-loop validation workbench for education loan decisioning. It generates a realistic test population, scores it through your model, quantifies bias across five dimensions, and closes the gap — with an audit trail your regulator can read.
Composite fairness score
Test run #EDU-2451 · 3,142 profiles
Approval Parity
0.85
Interest Gap
1.40%
Collateral Gap
18.6%
Fairness Score
68.4
Illustrative run · pre-mitigation 68.4 → post-mitigation 91.2
0+
Synthetic borrower profiles
evaluated per validation run
0
Bias dimensions covered
geography, income, gender, credit, edge cases
2–0 days
Fairness testing cycle
down from 4–6 weeks per model run
~2–0%
Residual approval gap
reduced from ~12–13% pre-mitigation
Indian lenders process over 700,000 education loan applications a year.
As those decisions shift to AI, the testing around them hasn't kept up. Three things break at once.
Coverage you cannot defend
Manual fairness testing reaches 50–100 hand-written cases. Your model will see every income band, every district tier and every thin-file edge case in production — the tail is exactly where bias hides.
A cycle too slow to iterate
A full fairness review runs 4–6 weeks per model version. By the time results land, the model has moved on — so validation becomes a checkbox at the end rather than a control in the loop.
Bias found after it costs you
Discovered post-deployment, a disparity is already a book of decisions: remediation, regulator questions under RBI fair lending expectations, and no structured record of who reviewed what, or when.
Five stages, running as a loop — not a one-off audit.
Every model version re-enters the cycle. Expert judgement feeds mitigation, mitigation feeds the next measurement.
Build a test population that looks like your real applicants.
One validation cycle, end to end.
This is the workbench an analyst uses — recreated here as a guided tour. Let it play, or click through the sidebar yourself.
Dashboard
Real-time bias metrics and fairness validation results
Approval Parity
Ratio of approval rates between groups
0.00
Interest Gap
Difference in interest rates (%)
0.00%
Collateral Gap
Difference in collateral requirements (%)
0.0%
Fairness Score
Overall fairness metric (0-100)
0.0
Bias Heatmap
Severity distribution across test dimensions
| Critical | High | Medium | Low | |
|---|---|---|---|---|
| Geographic | 3 | 5 | 2 | 1 |
| Income | 2 | 4 | 3 | 2 |
| Gender & Co-applicant | 1 | 3 | 4 | 2 |
| Credit Score Logic | 0 | 2 | 5 | 3 |
| Edge Cases | 2 | 3 | 2 | 4 |
Top Findings
Critical bias issues requiring attention
Rural applicants approved at a materially lower rate than urban applicants with equivalent merit
Interest rate premium applied to households below ₹12L despite identical repayment capacity
Female applicants asked for a co-applicant more often at the same credit profile
+ 5 more findings across 5 dimensions
Dashboard · The analyst opens a completed test run. All four fairness KPIs are failing their targets, and the heatmap shows where the damage is concentrated.
Demo mode — illustrative data, not a live environment.
Fairness you can put in front of a regulator.
Not a research notebook. A validation control that fits inside model governance, with output shaped for the people who sign off on it.
Validate before you deploy
Fairness testing moves from a post-hoc audit to a gate in the model release path. Every version is measured against the same targets before it ever prices a real application.
Evidence a regulator can read
Each cycle emits a before/after comparison with quantified parity, disparity and coverage metrics — the artefact your model risk committee and RBI fair lending review actually ask for.
A complete human review trail
Every finding carries the analyst who reviewed it, the severity they confirmed, the root cause they attributed and the mitigation it triggered. Attributable, timestamped, exportable.
Coverage at the tails
3,100+ profiles per run, weighted deliberately toward the cases that break models: thin-file applicants, self-employed co-applicants, seasonal income, border districts, Tier-3 geography.
Model-agnostic by design
The scoring layer sits behind an API. Point it at your existing underwriting engine, rules platform or ML model — the validation loop does not care how the decision is produced.
Built for Indian lending reality
CIBIL bands, Tier-1/2/3 geography, ₹8L–₹50L household income, co-applicant structures and education loan products — the test population reflects the applicants you actually underwrite.
A modular layer that drops into your stack.
Container-based, API-first, and horizontally scalable — the validation loop connects to any upstream decision engine rather than replacing it.
Analyst workbench
Next.js 14 · TypeScript · Tailwind
API layer
FastAPI · REST /api/v1 · WebSocket
Profile svc
Generation
Scoring svc
Fair / biased
Metrics svc
Parity engine
Gemini
via LangChain
Scikit-Learn
+ rule engine
Postgres
+ Redis cache
Celery handles large profile batches asynchronously; the WebSocket channel streams KPI updates to the dashboard as a run progresses.
API surface
Six functional domains plus a live metrics channel.
/authJWT employee login, token validation, sessions/profilesSynthetic generation, CSV/ZIP upload, listing/scoringTrigger fair or biased scoring runs/metricsParity ratios, disparity indicators, KPIs/feedbackHuman annotations and severity ratings/mitigationLaunch cycles, before/after comparisonws://…/ws/metricsLive fairness score streamingStack
Run your model through a fairness cycle.
Access is granted per institution. Tell us who you are and what you underwrite, and we'll set up an evaluation run against your decisioning logic.
- A scoped validation run on a synthetic population matched to your book
- Walkthrough of the findings with your model risk team
- Before/after mitigation report you can take to committee