Global Equipment Finance Scorecard
A D&B business layer plus a consumer-bureau guarantor layer, fitted once and deployed across 14 markets on six continents. Raw bureau scores cannot cross a border — percentiles can. Change the market and watch the same borrower quality arrive as a different number.
Deal & market
Segment decides which attributes are collected. Market decides how the guarantor score is read.
Attributes
Greyed rows are not collected at this segment — they fall to their missing bin, not an imputed value.
Points by layer
Highlighted rows are attributes the file could not answer.
| Attribute | Selected | Pts |
|---|
Same borrower, fourteen markets
The raw score that equals your applicant’s percentile in each market.
| Market | Bureau | Equivalent |
|---|
Designing a scorecard that crosses borders
Three things break when a scorecard leaves its home market. Only one of them is usually noticed.
1. The scales are incompatible — and percentiles fix it
FICO runs 300–850. CIBIL 300–900. Equifax Australia 0–1200. Credit Bureau Singapore 1000–2000. SCHUFA is a percentage. Even within the United Kingdom the three bureaus report on 0–999, 0–1000 and 0–710. A raw number is meaningless without knowing which bureau in which country produced it.
So the scorecard never bins a raw score. It converts each score to its percentile within that market’s own population and bins the percentile. A 759 in the US, an 887 in the UK, a 96 SCHUFA and a 1921 in Singapore are all the same applicant — the 60th percentile — and all score identically. The table above the fold does this live.
The distributions here are illustrative anchor points. In production you replace them with your own observed through-the-door population per market, which also means the normalisation self-corrects as a market’s credit quality shifts.
2. PAYDEX is the exception, and it is the reason to anchor on D&B
The business layer barely needs normalising. PAYDEX is a 1–100 index that means the same thing everywhere D&B or a Worldwide Network partner operates — 190+ countries and markets. Eighty means paying on terms in Ohio and in Osaka. The Failure Score and Composite Credit Appraisal travel the same way.
That asymmetry is the single most useful fact in building a global scorecard: the business layer is portable, the consumer layer is not. It is why this design leans on D&B for the backbone and treats the guarantor as a normalised overlay rather than the other way round. In this build the D&B layer carries an aggregate information value of 2.47 against 0.85 for the guarantor layer.
3. Availability varies — so missing has to be a value
Some markets have no meaningful consumer bureau. Some businesses never file financials, so there is no Composite Credit Appraisal. Some jurisdictions weaken personal guarantees to the point of irrelevance. A model that requires a complete record cannot be deployed globally.
Every attribute therefore carries an explicit missing bin with its own empirically estimated weight of evidence — no imputation. “No D&B file in market” is scored on the observed default rate of deals with no D&B file, which in this sample runs 5.97% against 3.54% where a file exists. The scorecard degrades gracefully instead of failing.
The same mechanism does the segmentation work. Small-ticket deals never collect financial statements, so leverage and DSCR simply sit in their missing bins. One model serves all three ticket segments — validated separately on each: Gini 0.62 small, 0.68 mid, 0.67 large.
The finding I did not expect
Discrimination is not uniform across markets. The same scorecard achieves a Gini of 0.70 in the United States and 0.49 in Nigeria. That is not a modelling failure — it is a direct consequence of data depth. US deals arrive with a D&B file 93% of the time and a bureau score 89% of the time. Nigerian deals arrive with 36% and 41%.
The practical implication is uncomfortable and worth stating plainly: a global scorecard is not equally good everywhere, and pretending otherwise is how you lose money in frontier markets. Thin-data markets need a lower reliance on score, a heavier reliance on collateral and structure, and ideally a locally-fitted model once volume permits. The market risk tier attribute captures some of this, but it prices the environment, not the model’s own blindness in that environment.
What is deliberately excluded
Recovery. Repossession timelines, legal enforceability and secondary-market depth differ enormously across these fourteen markets, but those drive loss given default, not probability of default, and mixing them into a PD scorecard is a common and expensive error. Equipment resale depth appears here only as an origination-quality signal; the recovery half belongs in a separate LGD model and in the pricing, which is where the RAROC framework picks it up.
What would change in production
Per-market population distributions replacing the illustrative anchors. Segment-specific models once each segment carries enough defaults to support one. Reject inference. Fair-lending and data-protection review per jurisdiction — GDPR, India’s DPDP Act and Brazil’s LGPD all constrain what may enter a model and what must be explainable. And a governance decision most groups get wrong: who owns the cut-off. A single global cut-off applied to markets with different data depth quietly exports credit policy from headquarters to whichever market has the thinnest files.
Educational model built on synthetic data across illustrative market distributions. Not a production scorecard, not a lending recommendation, and not fit for credit decisioning. PAYDEX and D&B are trademarks of Dun & Bradstreet; FICO of Fair Isaac; other bureau marks belong to their owners. Runs entirely in your browser — nothing is stored or transmitted.