Sectoral PD Reference Database for Industry Default Rates

Key benefits & value for the buyer

The Sectoral PD Reference Database turns fragmented public and proprietary default data into a structured, IFRS 9–friendly reference asset. Rather than building historical series from scratch, your credit risk team gets:

• Time savings: Deploy in days — not months — for model calibration, staging analysis, and sensitivity testing.
• Compliance readiness: Fields and disclosure notes aligned to ECL requirements, supporting external audit and regulator reviews.
• Comparability: Industry-standard sector definitions and benchmark bands to compare portfolio PDs with external norms.
• Traceability: Source annotations and methodology notes included to demonstrate data lineage during validation.

Use cases & real-life scenarios

Model calibration and validation

Use the database to populate benchmark PD curves for low-, medium- and high-risk industry groups. Example: calibrate lifetime PDs for a corporate loan segment by mapping obligors to sector codes and applying historical transition and default trends.

Staging / 3-stage model support

Combine sectoral default rates with borrower-level behavioural indicators to support movement between stages 1/2/3 and to quantify lifetime expected losses where internal history is limited.

Stress testing and scenario analysis

Run macro-linked scenarios using historical peaks and troughs per sector, or apply percentile shocks to sectoral PDs to estimate forward-looking ECL under adverse conditions.

Who is this product for?

The database is designed for institutions and teams that need robust sectoral PD benchmarks and historical default rate data that plug directly into IFRS 9 workflows:

• Retail, corporate and SME credit risk teams at banks
• Model validation and governance units
• Finance teams preparing financial statements and ECL disclosures
• Consultancies and auditors performing independent checks
• Insurance firms and lessors that maintain credit portfolios under IFRS 9

How to choose the right license & coverage

Choose by portfolio mapping, not by price alone. Consider:

• Geographic coverage: Single-country vs multi-country datasets depending on your portfolio footprint.
• Time horizon: Standard 10-year series vs extended 20+ year series for institutions needing long-run stress benchmarks.
• Industry granularity: Broad sector groups (e.g., manufacturing) or fine-grained NAICS/SIC-level splits for specialised portfolios.
• License type: Single-use validation pack vs enterprise license for ongoing model updates and multiple users.

If you are unsure, start with the standard package and add geographic or granularity modules as required — ECLReport provides assistance mapping your portfolio to the dataset.

Quick comparison with typical alternatives

Typical alternatives include building internal series from borrower histories, buying aggregated market reports, or using vendor PD matrices. The Sectoral PD Reference Database balances the trade-offs:

• Vs internal builds: Faster and fully documented; reduces modeler time and governance burden where historical defaults are sparse.
• Vs market reports: More granular and machine-readable; includes sector-level bands and percentile measures for rigorous validation.
• Vs PD matrix vendors: Focused on historical default rates and benchmarking for IFRS 9 rather than point-estimate commercial scoring products.

Best practices & tips to get maximum value

• Map your portfolio to the supplied sector codes before integration; this ensures correct benchmarking and avoids misclassification bias.
• Use the included percentile bands to define plausible override windows for management adjustments.
• Document any local adjustments and keep a change log — the package includes a template for governance notes.
• Combine with macro linkages (provided as a separate module) for forward-looking scenarios when preparing ECL provisioning under stress.

Common mistakes when buying/using sectoral PD data — and how to avoid them

• Mistake: Assuming sector averages apply uniformly across regions. Fix: Use regional modules or apply crosswalks included in the pack.
• Mistake: Over-reliance on a single historical period. Fix: Use percentile bands and multiple horizons included in the dataset.
• Mistake: Poor portfolio mapping. Fix: Follow the mapping checklist in the documentation and request support if needed.

Product specifications

• Coverage: 40+ industry sectors; regional and country modules available
• Historical depth: Standard 10-year series; extended 20+ year option
• Formats: Excel (.xlsx) with worksheets for each sector, CSV exports, and PDF methodology report
• Deliverables: Data files, methodology notes, mapping checklist, governance templates, sample model inputs
• License types: Single validation license, annual subscription, enterprise license
• Update frequency: Quarterly updates for recent defaults; annual full refresh
• Compatibility: Designed for integration with common credit risk models and ECL calculation templates

FAQ