Unified Data Architecture for Orbital Credit Risk
Two integrated engines, the Saryn OR engine for multidimensional risk scoring and the Saryn CR engine for Bayesian PD estimation, operating over a unified telemetry and financial data layer. All outputs are audit-ready and mapped to IFRS 9 and Basel III/IV frameworks.
Continuous ingestion of orbital telemetry, TLE propagation data, operator financial filings, regulatory status feeds, and space situational awareness datasets. Unified schema across 80+ operators and 12,000+ assets.
Google Cloud Run · MongoDB AtlasSeven-component risk scoring. Each component draws on a distinct data category: orbital mechanics, propulsion, power, communications, operator capacity, regulatory posture, and sovereign dependency. Components are combined and normalized against total credit exposure to produce a dimensionless Saryn OR Score. Aggregation methodology available under NDA.
Non-financial covariatesBayesian probability-of-default engine: calibrated priors for sparse-history assets, operator-class pooling, failure-conditioned estimation on a labelled set of historical failure events, model ensembling, and sequential updating across the 1, 3, and 5-year horizons.
40-case SR 26-2 BacktestOperator-level aggregation, concentration risk flagging, correlated failure modelling across constellation structures, and SPV tranche-level PD attribution. Supports structured finance due diligence and satellite-backed lending portfolio management.
SPV · Tranche · ConcentrationAudit-ready PDF report generation, structured JSON API for system integration, dashboard access for portfolio teams, and regulatory export formats mapped to IFRS 9 Stage classification and Basel III/IV Pillar II disclosure requirements.
REST API · PDF · DashboardSeven-Component Risk Scoring
OR Score produces a dimensionless risk score from seven independently scored components. Higher scores indicate elevated risk. Each component is bounded by an orbit-class-specific ceiling, and the combined score is normalised against total credit exposure so results are comparable across operators and orbits.
A failure in any single component propagates into the aggregate score, reflecting the physical reality that satellite asset failures are often single-point-of-failure events. Full component weighting and aggregation methodology is available under NDA.
Five-component Bayesian probability of default
For assets with short or zero default histories, calibrated priors prevent the common failure of assigning near-zero PD to young assets.
Assets are pooled by operator class (GEO comms, LEO SAR, MEO navigation, and similar) so information is shared across comparable assets, improving stability for thin-data assets while preserving asset-specific results.
Estimation is conditioned on a labelled set of historical on-orbit failure events, linking the risk components to realised default outcomes.
Independent model families are combined into a consensus probability of default across the 1, 3, and 5-year horizons, reducing single-model overfitting.
As new telemetry, financial filings, or operator events arrive, estimates update at the asset level without full retraining, supporting covenant monitoring and mark-to-market assessment.
Asset-Level Report
Full PDF output for a single satellite asset: OR Score component breakdown, posterior PD distribution at 1/3/5yr, data confidence score, comparables, and IFRS 9 Stage recommendation.
Portfolio Dashboard
Web-based interface for lenders and fund managers monitoring multi-asset exposure. Concentration risk heatmap, risk distribution histogram, and operator-level aggregation with drill-down to asset level.
API Integration
Structured JSON REST API for direct integration into lender credit systems, insurance pricing engines, or fund administration platforms. Supports batch queries across portfolios of up to 500 assets.
Operational Backtest
System-level validation across 40 historical default and near-default cases. Tests the full pipeline, Saryn OR scoring through Saryn CR probability-of-default output, against known outcomes using pre-event data. Zero missed defaults. Model risk framework aligned with SR 26-2 (US), PRA SS1/23 (UK), and the ECB Guide to Internal Models (EU).