Score Range and Interpretation

How Avra delivers credit scores, probability of default, and embeddings — and how to use them.

What you receive

Every credit prediction includes three artifacts:

Credit Score (0–1000)

A transformed, calibrated score for easy integration with your existing decision engines and policy rules.

1000 = lowest risk (probability of default approaches 0%)
0 = highest risk (probability of default approaches 100%)

Probability of Default (PD)

The raw probability behind the score — calibrated to your delinquency definition.

Direct interpretation — “this entity has a 15% chance of defaulting under your definition”
Pricing and reserves — calibrated probabilities for risk-based pricing, IFRS 9 staging, and capital allocation
Multi-horizon — PD is returned across 30, 60, 90, 180, and 365-day windows so each decision uses the horizon that matches its policy

Avra Embeddings

1024-dimensional representations of each entity.

Similarity analysis — find entities with similar risk profiles
Feature engineering — enhance your own models with relationship-aware features
Custom analytics — segmentation, monitoring, and anomaly detection

The 0–1000 score is derived from the underlying probability of default through a calibrated transformation. The relationship is monotonic — a higher score is always lower risk — and stable across model versions, so policy thresholds you set today continue to hold meaning as the underlying foundations improve.

{
  "score": 842,
  "pd_30d": 0.004,
  "pd_60d": 0.006,
  "pd_90d": 0.011,
  "pd_180d": 0.024,
  "pd_365d": 0.051,
  "risk_band": "low",
  "key_factors": [
    "Stable counterparty network",
    "Positive trajectory over last 6 months",
    "Sector health above baseline"
  ]
}

Multi-horizon PD fields

Field	Horizon	Use case
`pd_30d`	30 days	Short-term liquidity, payment timing
`pd_60d`	60 days	Trade credit, early delinquency
`pd_90d`	90 days	Standard credit bureau equivalent
`pd_180d`	180 days	Medium-term portfolio planning
`pd_365d`	365 days	Annual loss forecasting, IFRS 9 staging

Each horizon is independently calibrated. The 30-day PD is not a scaled 365-day PD — different signals matter at different time scales, and the model exposes them separately so each policy can pick its own window.

Defining delinquency

The definition of a “bad” outcome is defined by you. During onboarding we work with you to label your historical accounts as good or bad based on your operational definition. Common examples:

MOB 6 > 30 — account is more than 30 days past due in the sixth month on book
FPD 90 — first payment default after 90 days
Charge-off — debt written off as a loss

The downstream credit model is trained on top of your RFM using your labels. The 0–1000 scale is calibrated to reflect the probability of your specific outcome, not a generic default definition.

Score bands

To simplify decision-making, scores group into operational risk bands. The table below is a general guide; exact PD per band shifts with your delinquency definition, but the monotonic relationship (higher score = lower risk) always holds.

Score Range	Risk Band	Interpretation & Recommended Action
900 – 1000	Exceptional	Prime profile; minimal risk. Suitable for automated approval, highest limits.
800 – 899	Low Risk	Very strong profile. Confidently approve with favorable terms.
700 – 799	Moderate Risk	Good profile. Generally safe to approve, may consider standard terms.
600 – 699	Medium Risk	Warrants caution. May require additional review, lower limits, or collateral.
400 – 599	High Risk	Significant risk of default. Requires strict terms, guarantees, or denial.
0 – 399	Very High Risk	Extreme risk. Not recommended for credit extension.

Why probability matters more than the score alone

The 0–1000 score is convenient for decision rules. The underlying probability is what gives you the business intelligence to:

Price accurately — set rates based on actual expected loss
Manage portfolios — calculate reserves and capital requirements
Monitor trends — track how risk evolves over time
Compare segments — understand variations across customer types