The Capital Signal Engine™ constructs an auditable, reproducible financial record of capital exposure from adjudicated paid claims data — purpose-built for medical stop-loss underwriting.
Every medical stop-loss stakeholder shares a common challenge: the stop-loss and shock-loss analytics currently available rarely provide a complete and financially accurate picture of capital exposure when a catastrophic claimant event occurs. Population health estimates, clinical risk scores, and dashboards fall short of what stop-loss capital governance actually requires — a deterministic, auditable, and financially grounded capital truth.
Sound capital governance is distinct from care management, cost containment, and clinical analytics because the organizing question is not “how is medical cost reduced?” but rather “what is the exact financial position of this portfolio relative to the contractual obligations that govern it, and can that position be demonstrated, reproduced, and defended?”
Current MSL analytics provide probabilistic estimates derived from clinical proxies such as diagnosis codes, DRG classifications, disease risk scores, and utilization patterns. These describe the nature of a medical condition but carry no reliable or consistent relationship to the actual dollars paid on behalf of a covered member. This produces friction during health plan sponsor or captive board reviews of program year exposure, renewal negotiations where prior year loss development must be defended, and in reinsurance placements where counterparties must assess whether a program’s disclosed exposure is credible.
Not estimates. Not proxies. A defensible financial truth.
Fragmented, probabilistic, and non-reproducible.
Deterministic, auditable, and financially grounded.
These principles are embedded in our architecture and govern every calculation, output, and decision the Capital Signal Engine™ makes.
Each layer builds on the outputs of the previous one. Once a financial value is computer and locked, no downstream logic can alter it. Financial truth, once established, is permanent.
Constructs the definitive case-level financial record from adjudicated paid claims data.
Analyzes how capital accumulates and why risk concentrates near the attachment point.
Stress-tests the portfolio under adverse scenarios to quantify capital resilience.
Evaluates third-party liability recovery to produce a net, recovery-adjusted capital exposure.
The platform follows a structured, deterministic sequence — constructing financial truth at the case level, detecting recovery signals across domains, producing governance-grade outputs, and ensuring every result is fully auditable.
A catastrophic claim event isn’t an aggregate line.
It’s an economic accumulation of multiple claim lines spanning providers, service categories, and payment dates.
The Capital Signal Engine™ constructs deterministic case-level economic events by aggregating all paid claims associated with a single loss resolution.
All capital evaluation — including severity, attachment proximity, and routing — occurs at the case level.
The platform applies a proprietary TPL Signal Registry™ built on years of recovery data and claims intelligence. Signal detection is deterministic: a case either meets defined criteria or it does not.
Signals are evaluated only on financially qualified cases — after capital accumulation has been established.
Every output is structured to serve as an auditable capital record for underwriting committees, captive boards, and reinsurance counterparties.
Every execution run is bound to a defined input dataset, parameter set, and versioned logic framework. Outputs are reproducible immediately or estimated—never overwritten.
No downstream logic can alter established capital values. Every output is traceable to its originating data and execution context.
