The four-fifths rule — also called the 80% rule — is the standard test for disparate impact in credit decisioning. If the approval rate of any protected group is less than 80% of the approval rate of the most-favoured group, the difference constitutes prima facie evidence of disparate impact. Most Indian lending institutions have never run this test. Those that have run it once will not be able to show it was run last month.
What Disparate Impact Means in Credit — and Why It Is Legally Distinct From Intentional Discrimination
Intentional discrimination — treating a borrower differently because of their gender, religion, caste, or other protected characteristic — is obviously illegal and universally condemned. Disparate impact is harder to see, harder to prove, and harder to defend against: it occurs when a facially neutral policy or practice has a disproportionately adverse effect on a protected group, regardless of intent.
A credit policy that requires a minimum bank account age of 36 months may seem entirely neutral. But if women in the target market have substantially shorter formal banking histories than men — because they entered the formal financial system more recently — that policy creates disparate impact on female applicants without any discriminatory intent whatsoever. The intent is irrelevant; the effect is what the law measures.
Indian lending law has not yet codified the four-fifths rule with the explicitness of the US Equal Credit Opportunity Act — but the RBI's Fair Practices Code, the Constitutional protection against discrimination, and the emerging DPDP Act framework collectively create an environment where disparate impact analysis is not just best practice. It is the standard a well-governed institution should be able to demonstrate when asked.
The Four-Fifths Calculation: Step by Step
The Monthly Disparate Impact Dashboard
| Group | Applications | Approved | Approval Rate | 4/5 Ratio vs Most-Favoured | 80% Threshold | Status |
|---|---|---|---|---|---|---|
| Salaried Male (baseline) | 4,841 | 3,311 | 68.4% | 1.000 (baseline) | — | Baseline |
| Salaried Female | 2,188 | 1,352 | 61.8% | 0.904 | > 0.80 ✓ | Pass |
| SE Male | 3,412 | 1,849 | 54.2% | 0.792 | > 0.80 — borderline | Watch — 0.008 below threshold |
| SE Female | 1,284 | 617 | 48.1% | 0.703 | < 0.80 ✗ | Fails — Board escalation |
| Tier 2 Geography | 5,210 | 3,199 | 61.4% | 0.897 | > 0.80 ✓ | Pass |
| Age 22–28 (First-time) | 1,840 | 1,075 | 58.4% | 0.854 | > 0.80 ✓ | Pass |
| Name cluster — minority-associated | 2,180 | 1,381 | 63.3% | 0.926 | > 0.80 ✓ | Pass |
Beyond Approval Rates: The Three Additional Tests the AI Runs
The four-fifths rule applied to approval rates is the most commonly understood disparate impact test. The Fair Lending AI runs three additional tests on the same population because approval rate parity alone does not guarantee fair lending outcomes.
Are Approved Loans of Similar Size Across Protected Groups?
Among approved applicants with matched income, bureau score, and LTV request, does any group consistently receive smaller loan amounts? SE Female approvals average ₹38.4L versus ₹53.8L for Salaried Males on matched cohorts — a 28.6% gap. The four-fifths test on loan amount (0.714) also fails the threshold, independently of the approval rate finding.
Are Similarly-Profiled Borrowers Getting the Same Rate?
Among approved applicants with identical risk bands, are interest rates consistent across protected groups? A 15–25 basis point premium on SE Female borrowers in Band B+ versus Salaried Male borrowers in the same band — where the risk band explicitly controls for credit risk — indicates pricing disparity that is not justified by the risk assessment. The Fair Lending AI flags a mean rate difference of 18bps for Band B+ SE Female borrowers.
Same Profile, Different Group — Different Outcome?
The most rigorous test: construct matched pairs of applicants who are identical across all financially relevant variables (income, bureau score, LTV, employment tenure, property location) but differ on a protected characteristic. If the matched-pair rejection rate for SE Female applicants is 19.4% higher than for Salaried Male applicants with identical financial profiles, the residual gap after controlling for all legitimate factors is the clearest possible evidence of structural model bias.
What Happens When a Test Fails
A four-fifths failure is not treated as a statistical curiosity — it triggers a defined escalation protocol. The Fair Lending AI generates an escalation brief within 24 hours of detecting the failure: the specific metric that failed, the magnitude of the disparity, the number of borrowers affected, a matched-cohort analysis distinguishing legitimate risk difference from structural bias, and a recommended remediation pathway. The brief goes to the CCO within 24 hours, the CRO within 48 hours, and the Board Risk Committee at the next scheduled meeting. The finding is logged in the fair lending register with an open remediation action that cannot be closed until the disparity is within threshold.
The Four-Fifths Rule Does Not Require Intentional Discrimination to Apply
The institution that has never run this test does not know whether it passes or fails. The institution that ran it once at model deployment does not know whether it still passes after 18 months of portfolio evolution. The institution that runs it monthly, for every protected group, across four metrics — that institution knows. And knowing is the difference between a governance function that is operating and one that is merely described in policy documents. Fair lending compliance is not a policy claim — it is a measurement practice. The Fair Lending AI makes the measurement routine, so the claim becomes provable.