---
title: Credit Intelligence
description: Dynamic credit risk assessment powered by two pre-trained foundation models and your business outcomes.
sidebar:
  label: Overview
---

## 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.

:::info
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:

1. **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.

2. **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.

3. **Downstream credit model**

    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

**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](/pt-BR/use-cases/credit-intelligence/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