India lacks a standardised, individual-level health risk metric equivalent to the CIBIL credit score, leaving insurers to price risk largely on self-declaration and occasional point-in-time screening. This paper constructs a prototype health risk score for the Indian market — as a diagnostic exercise rather than a commercial product — to identify the data gap and quantify the cost of the data gap. We compare a Full Clinical Model incorporating laboratory biomarkers against a Restricted Predictor Model without them; the clinical model achieves a PR-AUC 33% higher, with six of the ten most predictive variables originating from laboratory results. Yet in practice fewer than 5% of policyholders undergo screening at underwriting, and existing medical records remain largely un-digitised and fragmented. We argue that this data deficit sustains a self-reinforcing "Market-Failure Feedback Loop": pervasive information asymmetry prevents refined risk pricing, forces an implicit cross-subsidy from healthy to less-healthy policyholders, suppresses low-risk participation, and surfaces as claim denials that deepen a public trust deficit and entrench under-penetration. Drawing on the precedent of credit bureaus in Indian lending, the paper contends that closing this gap requires a government mandate for standardised health-data reporting. It further examines the central risk such a mandate creates — exclusion of high-risk individuals — and outlines protections (risk pooling, policy design, constrained use, and universal-coverage backstops) intended to ensure that fuller disclosure leads to fair pricing rather than denial of coverage.