Beyond CIBIL Scores:
How Federated Data Is Enabling Inclusive Credit Decisioning in India

The Bureau Score Blind Spot

CIBIL's scoring methodology evaluates formal credit behaviour: card usage, EMI repayment, credit utilisation, account age. For borrowers with established credit histories, this is reliable. The problem is coverage. UPI transaction patterns reflecting real spending, GST filing history showing MSME revenue trajectories, utility payment records, Account Aggregator bank statement data, and ULI data services spanning land records to dairy production — none of these appear on the left side of the bureau score.

The Alternative Data Explosion

India now has more alternative data signals available for credit assessment than any other market. UPI processes over 10 billion monthly transactions. ULI connects 64 lenders to 136+ data services. The Account Aggregator framework enables consent-based sharing of bank statements. The data exists. The question is how to use it responsibly — because simply layering alternative signals on top of bureau scores in a centralised model creates three new risks: bias amplification, consent erosion, and explainability failure.

Why Federation Is the Architectural Answer

A federated architecture queries data in place — at the source system — without copying it into a central repository. The data never moves; the query moves. Data sovereignty is preserved, lineage remains intact (every decision can be traced back to the actual source record at the time of the query), and governance is enforced at the access layer. Federation resolves simultaneously the problems of sovereignty violation, lineage loss, and governance sprawl that centralisation creates at ULI scale.

The Federation-Inclusion-Governance Triangle

Inclusive credit decisioning requires all three vertices. Inclusion: more data sources means more borrowers assessed — UPI, GST, utility, AA, ULI each bring previously invisible borrowers into the assessment frame. Governance: every data input must be traceable, every decision explainable, every consent verifiable. FREE-AI, DPDPA, and RBI lending guidelines all converge on this requirement. Federation: query in place without centralising, preserve sovereignty and lineage at the source.

Why Governance and Inclusion Are the Same Priority

A persistent framing positions financial inclusion and regulatory governance as competing objectives. This framing is architecturally false. Ungoverned AI does slow down inclusion — because models that cannot explain their decisions, trace their data inputs, or demonstrate fairness will not pass supervisory review, will not earn borrower trust, and will not survive the regulatory environment that FREE-AI, DPDPA, and the EU AI Act are collectively creating.

ULI is envisioned as a Digital Public Infrastructure in the lending space, designed to integrate technology, data, and policy into one seamless platform.

— M. Nagaraju, Secretary, Department of Financial Services