Step 01
Step 1
Partner sends transaction history and KYC; Mukodi returns a risk score with SHAP-level feature attributions.
AI InsightsRetail + B2Bpartial· marketing-claim · b2b-credit-risk-shap
Credit risk + SHAP + counterfactuals.
Explainable risk scores for partner lenders — every score ships with feature attributions.
The problem
Why this exists.
Black-box credit scores have made a generation of Indian borrowers fight unwinnable disputes with no visible reason for a decline.
Lenders need scores they can defend in writing; borrowers need plain-language reasons; regulators need both.
How it works · 3 steps
From input to outcome.
- Step 02
Step 2
Every score includes counterfactuals — 'would this borrower clear at 50k income instead of 40k?'
- Step 03
Step 3
Every meaningful score passes through the Courtroom — Proposer, Defender, Judge — before it reaches the partner.
Under the hood
The models and routes behind it.
Models called
API routes
- POST /api/v1/predictions
- POST /api/v1/b2b/credit-risk
Compliance posture
- Automated decisions carry a plain-language explanation — SHAP attributions translate to borrower-readable reasons (DPDP §11 access right).
- Courtroom signs every consequential output under courtroom-replayable-audits.
- Lender always makes the final call — Mukodi outputs a score, not a yes/no.
Interactive demo · mocked at the edge
See it work.
Try the Courtroom (mock)
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What it's not
Mukodi outputs an explainable risk score, not a yes/no decision. Lenders make the final call and own the regulator-facing record.
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