Beyond static scores
Traditional credit scoring looks at an entity in isolation — payment history, registered debts, basic attributes. It misses the context that actually predicts outcomes. Credit risk is relational. An entity’s risk profile depends on its counterparties, its position in supply chains, the health of its network. A company with a perfect payment history becomes high-risk when its main customer is failing.Avra’s foundations capture relationship patterns that traditional systems miss — multi-hop network effects, behavioral trajectories, and latent risk signals.
How Avra assesses credit
We compose two pre-trained foundations with your business reality:Graph Foundation Model
The GFM already understands the relational economy — entities, their counterparties, supply paths, judicial events, sector dynamics. Network position and behavioral trajectories are learned before we ever see your portfolio.
Your Relational Foundation Model
Your RFM brings your portfolio’s reality — customers, payment behavior, the connections that matter for your specific lending or trade-credit operation.
What you receive
Credit score and probability of default
Every prediction returns both:- A 0–1000 credit score for easy integration with existing decision engines and policy rules.
- A calibrated probability of default (PD) for risk-based pricing, capital allocation, and portfolio analytics.
Multi-horizon PD
PD is delivered across multiple time horizons so each decision uses the window that matches its policy:| Horizon | Typical use case |
|---|---|
| 30 days | Short-term liquidity, payment-timing risk |
| 60 days | Trade credit, early delinquency signals |
| 90 days | Standard lending decisions |
| 180 days | Medium-term facilities, portfolio planning |
| 365 days | Annual loss forecasting, IFRS 9 staging |
Fine-tuned PD
Beyond the standard horizons, downstream credit models are calibrated to your delinquency definition. Common shapes:| Definition | Description |
|---|---|
| FPD | First Payment Default — never made first payment |
| Over30MOB6 | 30+ days past due by month 6 on book |
| Over60MOB12 | 60+ days past due by month 12 on book |
| Charge-off | Written off as a loss |
Risk drivers
API responses include the key factors influencing each score — the network signals, behavioral patterns, and entity attributes that drove the prediction. Useful for review queues, customer communication, and regulatory transparency.Dynamic risk monitoring
Beyond point-in-time scoring, Avra provides trajectory analysis — how entities move through the representation space over time:- Early warning — spot entities moving toward delinquent patterns before traditional metrics show problems.
- Portfolio monitoring — track aggregate risk movements across your book.
- Intervention timing — identify the moment to act, not the moment to react.
How we measure performance
We evaluate models using metrics that together give a complete picture:- ROC-AUC — primary discrimination across all decision thresholds.
- PR-AUC — critical when defaults are rare and false alarms expensive.
- KS statistic — maximum separation between good and bad distributions, widely used by financial institutions for model validation.
Why this works
Cold-start solved
Traditional models struggle with new or data-scarce entities. The graph approach infers risk from network connections, even with limited direct history.
Context-aware
Trained on your RFM, scores reflect your business relationships. A pattern normal for one segment might be high-risk in another.
Explainable
API responses include the key factors influencing each score — transparency for decisions and for compliance.
Temporal
Not just who an entity is today, but how they are evolving. Trajectory matters more than snapshots.
From scores to policy
Scores and probabilities are decision inputs. To operationalize them you typically bucket entities into homogeneous risk groups so policy thresholds map cleanly onto approve / review / deny decisions and pricing tiers. See Risk Bands for the canonical approach to optimal binning, monitoring bin stability over time, and detecting drift.Integration
Credit intelligence is available through:- Real-time API — sub-second scores for underwriting decisions
- Batch processing — score entire portfolios on a schedule
- Embeddings — use Avra representations as features in your own credit models