Use Case Private Credit Risk

Most private infrastructure debt is
effectively unrated.

Investors hold large numbers of private infrastructure debt positions with no public credit rating, no liquid secondary market, and no standardised peer benchmark — leaving risk teams to fall back on manual, case-by-case assessments that are slow, inconsistent, and impossible to scale across the book.

The Challenge

Unlike listed bonds, private infrastructure debt lacks the basics

Investors hold large numbers of private infrastructure debt positions. Unlike listed bonds, these instruments lack public credit ratings from S&P, Moody's or Fitch, a liquid secondary market for price discovery, standardised peer benchmarks for comparison, and transparent deal-level spread and performance data.

  • Public credit ratings from S&P, Moody's or Fitch
  • A liquid secondary market for price discovery
  • Standardised peer benchmarks for comparison
  • Transparent deal-level spread and performance data

Risk teams fall back on manual, case-by-case assessments — slow, inconsistent, and impossible to scale across the portfolio.

What a solution must deliver

A scalable way to produce credit ratings and PDs for every line in the book

  • Consistent methodology across all exposures
  • Fast enough to run on the full portfolio
  • Transparent — every grading traceable to inputs
  • Market-aligned with public ratings where both exist

The Solution

Shadow credit ratings — a model-based alternative

A reduced-form credit risk model turns the company-level data investors already hold into a per-company credit view — probability of default, shadow credit grade, peer comparables, and sensitivity to input changes.

1. Inputs

Company-level data investors already have

  • Financial ratios (leverage, DSCR, ICR)
  • Cash flow available for debt service
  • Firm age, size, sector (TICCS®)
  • Country / economic region
  • Corporate vs. project structure
2. The Model

Reduced-form credit risk model

  • Calibrated on 700+ firms with 25+ years of observed credit behaviour
  • Separate specifications for corporate and project debt
  • Links financials and macro variables directly to observed defaults
  • Data refreshed as and when the company reports it
3. Outputs

Per-company / segment credit view

  • Probability of Default (PD)
  • Shadow credit grade (IG / NIG bucket)
  • Peer comparables
  • Sensitivity to input changes
  • Transition / migration view

Why the model works

Built using one of the broadest private infrastructure debt datasets

9,100+

Infrastructure firms in the universe dataset

2,000+

Senior debt instruments with observed data

25+

Years of observed credit behaviour

25

Countries across global infrastructure

Validated against public ratings

87% alignment with rating agencies over five years when classifying the same issuer as Investment Grade vs. Non-Investment Grade.

Model average PD ranges closely match observed default rates across corporate, project and utility segments (S&P 2022 study).

How It Works

Using inputs investors already track

Every risk driver in the model is observable from financial statements and deal terms investors already collect. No proprietary data required.

Risk DriverApplies ToDirection of Effect on Credit Risk
Interest Coverage RatioCorporateHigher coverage → lower default risk
Leverage / Debt-to-Asset RatioCorporate & ProjectHigher leverage → higher default risk
Cash Flow Available for Debt ServiceProjectStronger DSCR → lower default risk
Cash & Quick Ratio (liquidity)Corporate & ProjectMore liquidity → lower default risk
Return on AssetsProjectHigher profitability → lower default risk
Firm Size & AgeCorporateLarger / older firms → more resilient
TICCS® Business ModelCorporate & ProjectCaptures cash flow stability
Economic Region & Risk-Free RateCorporate & ProjectLocal macro & cost-of-capital effects

All variables are observable from financial statements and deal terms investors already collect. No proprietary data required.

Worked Example

From loan file to shadow rating, in one step

UK renewable energy project loan · XXX-00417

Drop in a firm's key parameters — country, sector, structure, latest financial ratios, age and credit event history — and the model returns a full credit view instantly.

Inputs

Single firm, multi-period
Loan IDXXX-00417
Value Date31/12/2025
CountryUnited Kingdom
TICCS SectorRenewables
StructureProject finance
Firm age (years)11
DSCR (CFADS)1.45
Leverage ratio0.68
Cash ratio0.22
Quick ratio0.95

Outputs

Full credit view, returned instantly
Shadow PD (1-yr)
1.62%
vs. project finance avg. of 2.0%
Shadow Grade
Non-Investment Grade
Market equivalent
SIPA Risk Bucket
Medium
Between low-risk core and high-yield

▲ Pushing PD up

LeverageDebt-to-asset above sector norm
SectorRenewables carry elevated default risk
Firm ageYoung firm — thinner history

▼ Pulling PD down

DSCRStrong cash flow coverage of debt service
Cash ratioHealthy short-term liquidity buffer
CountryStable macro & regulatory environment

Scaling Up

From one loan to the whole book

The same model runs across an entire loan book at each point in time, and reconstructs a full credit history for any single firm — giving investors a single, consistent view of credit risk across every exposure.

Assess the credit risk of an entire loan book

A single, consistent view of credit risk across every exposure in the book — sectors, countries and structures side by side, run from one spreadsheet or API call.

321
firms in the book
1
spreadsheet / API call, one click

Reconstruct a full credit history for any single firm

Credit transition tracking, early-warning signals and refinancing-risk analysis — the story of how a loan's risk has evolved, one firm rated every year it reported financials.

15
years of credit history
1
firm, rated every period
Value DateCompany IDCountrySectorStructureAge (yrs)Shadow PDMarket-Eq. GradeBucket
31/12/2025XXX-00417GermanyRenewablesProject121.62%Non-Investment GradeMedium
31/12/2025XXX-00418SingaporeTransportProject280.84%Investment GradeLow
31/12/2025XXX-00419UKNetwork UtilitiesCorporate410.19%Investment GradeLow
31/12/2025XXX-00420AustraliaEnergy & WaterCorporate151.91%Non-Investment GradeMedium
31/12/2025XXX-00421ItalySocialProject61.44%Non-Investment GradeMedium

Each row is a different infrastructure firm — countries, sectors, structures and ages vary across the book, same value date, same model, one run. 315 more rows not shown.

What Investors Get

Concrete benefits of adopting SIPA's shadow credit ratings

01

Portfolio-wide visibility

Every loan carries a comparable PD and grade. The credit view of the book is no longer dependent on which loans happen to be rated.

02

Faster decisions, lower effort

Run thousands of instruments in seconds. Free up analyst time for judgement calls rather than spreadsheet mechanics.

03

Regulatory alignment

Outputs usable under Solvency II, PRIIPs and similar frameworks that expect PD / LGD-style metrics for unrated exposures.

04

Scenario & stress testing

Flex any input — leverage, DSCR, country, rate environment — and see the portfolio's PD response immediately.

05

Credible benchmarking

Anchor an internal view to observed behaviour across 700+ infrastructure firms globally, not a handful of public comparables.

06

Transparency & audit trail

Every rating decomposes into its input drivers, making conversations with auditors, boards and regulators straightforward.

See your book's shadow credit ratings

Our team can run your private infrastructure debt book through the model and walk through the results.

Book a demo