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There is no single right way to score a lending pool’s risk — a bad-debt accountant, a simulation shop, and a machine-learning team will each answer the question differently, and each answer is informative. Rather than collapse them into one opaque number, Alterscope computes several independent scores using established, published methodologies and presents them side by side, each with its own units and provenance, so you can see where they agree and where they diverge.

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How scores are surfaced

The pool risk-scores endpoint returns the latest score from each methodology for a given pool. Critically:
  • Each score is presented as-is — no blending, no averaging, no normalization. Different methodologies answer different questions in different units (a dollar figure, a probability, a percentage), so collapsing them would destroy information. You get each firm’s number with its own score_unit, the block and time it was computed as_of, a confidence, the input summary, and a link to the published methodology.
  • Stale scores are withheld, not shown. Each score is checked against a freshness window (seven days). If a score is too old to trust, the endpoint returns no score for that methodology rather than a stale number — the same freshness-first discipline applied everywhere on the platform.

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The methodologies

Each score is Alterscope’s own implementation, built to follow the firm’s publicly documented methodology. These are independent reconstructions from public research — Alterscope does not call these firms’ APIs or resell their proprietary outputs. Each is labeled with the methodology and version it implements.

RiskDAO-style bad debt

Estimates the pool’s bad debt — the shortfall where a borrower’s debt exceeds the liquidation-adjusted value of their collateral — summed across underwater positions. Unit: USD. Follows the RiskDAO bad-debt methodology.

Gauntlet-style agent simulation

A Monte-Carlo agent-based simulation that runs many price paths and measures the fraction in which positions cross into insolvency. Unit: probability of insolvency. Follows Gauntlet’s published risk methodology.

Block Analitica-style vault liquidation

A machine-learning model that predicts the probability a position is liquidated within 24 hours, aggregated to a pool-level score. Unit: probability. Follows Block Analitica’s vault-liquidation model.
Each methodology may run multiple versions in parallel during transition windows; the response always names the exact methodology_version behind a number so results are reproducible and comparable over time.

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How to read divergence

Because the scores measure different things, divergence is signal, not noise:
  • A high RiskDAO-style bad-debt figure tells you losses may already be embedded in the pool.
  • A high Gauntlet-style insolvency probability tells you the pool is vulnerable under stress even if healthy now.
  • A high Block Analitica-style liquidation probability tells you near-term churn is likely.
Reading them together — rather than as one blended score — is the point. Each links to its source methodology so you can judge the approach yourself.

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What we publish vs. withhold

PublishedWithheld
Which methodologies are implemented, and their public sourceExact internal parameters of each reconstruction
The unit, version, freshness window, and inputs of each scoreTrained model internals (for the ML-based score)
That scores are surfaced independently with no blending—

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Where this shows up

Pool risk scores are returned by the pool risk-score endpoint in the API reference, with freshness and quality metadata on every response. Coverage varies by pool and methodology — which pools currently carry which scores is documented under Coverage & gaps. The boundaries of these reconstructions are on the Limitations page.