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What you receive

Every credit prediction includes three artifacts:
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%)
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
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

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How the score is constructed

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"
  ]
}

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Multi-horizon PD fields

FieldHorizonUse case
pd_30d30 daysShort-term liquidity, payment timing
pd_60d60 daysTrade credit, early delinquency
pd_90d90 daysStandard credit bureau equivalent
pd_180d180 daysMedium-term portfolio planning
pd_365d365 daysAnnual 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.

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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 6th 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.

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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 RangeRisk BandInterpretation & Recommended Action
900 – 1000ExceptionalPrime profile; minimal risk. Suitable for automated approval, highest limits.
800 – 899Low RiskVery strong profile. Confidently approve with favorable terms.
700 – 799Moderate RiskGood profile. Generally safe to approve, may consider standard terms.
600 – 699Medium RiskWarrants caution. May require additional review, lower limits, or collateral.
400 – 599High RiskSignificant risk of default. Requires strict terms, guarantees, or denial.
0 – 399Very High RiskExtreme risk. Not recommended for credit extension.

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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