> ## Documentation Index
> Fetch the complete documentation index at: https://docs.avra.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Credit Intelligence
> Dynamic credit risk assessment powered by two pre-trained foundation models and your business outcomes.
## 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:
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 RFM brings your portfolio's reality — customers, payment behavior, the connections that matter for your specific lending or trade-credit operation.
A task-specific model is trained on top of both foundations using your delinquency definitions and outcomes. Signal from training feeds back into your RFM, making the next iteration sharper.
## 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.
The score is derived from the PD — monotonic, stable across versions, and aligned with your delinquency definition.
### 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 |
Each horizon is independently calibrated — the 30-day PD is not a scaled version of the 365-day PD. Different signals matter at different time scales.
### 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 |
You define what "bad" means; the downstream model learns it. The reported PD is the probability of *your* event, not a generic default.
### 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
Traditional models struggle with new or data-scarce entities. The graph approach infers risk from network connections, even with limited direct history.
Trained on your RFM, scores reflect your business relationships. A pattern normal for one segment might be high-risk in another.
API responses include the key factors influencing each score — transparency for decisions and for compliance.
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](/solutions/credit-score/homogeneous-groups) 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