Unified Data Architecture for Orbital Credit Risk

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.

Seven-component multiplicative risk scoring. Each component draws on a distinct data category: orbital mechanics, propulsion, power, communications, operator capacity, regulatory posture, and sovereign dependency. Components are multiplicatively combined and normalized against total credit exposure to produce a dimensionless SIGMA score.

Five-component Bayesian stack: Beta-Binomial conjugate priors for sparse-history assets, hierarchical Bayesian priors pooling across operator class, EM-fitted conditional probability tables on 881 labelled events, Bayesian Model Averaging across three model families, and sequential online updating.

Operator-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.

Audit-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.

For assets with sparse or zero default histories, a Beta-Binomial conjugate prior is constructed using the Moody's Annual Default Study as the hyperparameter source. This corrects the common failure mode of assigning near-zero PD to assets with short track records.

Assets are pooled into operator class groups (GEO comms, LEO SAR, MEO navigation, etc.). Hierarchical priors allow information sharing across assets within the same class, improving estimation stability for thin-data assets while preserving asset-specific posterior updating.

Expectation-Maximisation fitting on 881 labelled failure events. CPTs encode the conditional probability of asset default given each SIGMA component state. The EM procedure handles the partially-observed nature of on-orbit failure data.

Three model families (deterministic rules engine, gradient-boosted ML predictor, and Bayesian Network) produce independent PD estimates. BMA combines these weighted by posterior model probability, reducing single-model overfitting risk across the 1, 3, and 5-year horizons.

As new telemetry, financial filings, or operator events arrive, posterior distributions update without full model retraining. This enables real-time PD revision at the asset level, critical for covenant monitoring and mark-to-market risk assessment.