RiskCalc (Moody's)
Credit risk assessment model from Moody's.
About RiskCalc (Moody's)
RiskCalc runs Moody's proprietary default prediction models against private company financial statements and outputs a probability of default score, an expected loss estimate, and a credit quality ranking benchmarked against Moody's rated universe. Day-to-day, a credit analyst or finance director feeds in balance sheet and income statement data, and RiskCalc returns a quantitative credit score without requiring a public rating or market-traded instruments. It fills the gap between back-of-napkin ratio analysis and a full credit committee build-out. The tool fits commercial lenders, credit unions, and corporate treasury teams with recurring exposure to unrated private borrowers. Portfolio analytics let you roll up individual obligor scores and track concentration risk or migration over time. If your firm underwrites 50 or more private company credits annually and needs a defensible, auditable methodology, RiskCalc is built for that workflow. Where it falls short: the model is calibrated on North American and select international markets, so it performs worse on thin-data geographies. Excel integration exists but feels bolted on — high-volume users need the Moody's Analytics Platform, which carries its own cost and onboarding overhead. Smaller lenders doing fewer than a dozen deals a year will find the pricing hard to justify against simpler scoring tools like Sageworks or even a well-built spreadsheet model.
Best for
Lenders and finance teams assessing credit risk of private companies using quantitative models
Key Features
- Probability of default modeling for private companies
- Credit risk scoring using financial statement data
- Portfolio-level credit risk analytics
- Private company financial benchmarking
Pros & Cons
Pros
- Outputs a probability of default tied to Moody's rated universe, giving private company scores a recognizable benchmark auditors and regulators accept
- Handles sparse financial data common in private companies — the model is explicitly trained on non-public firms, not adapted from public-market inputs
- Portfolio-level aggregation lets credit teams track obligor migration and concentration exposure across a book, not just one deal at a time
- Financial statement benchmarking shows how a borrower's ratios rank within its industry peer group, useful for credit memo documentation
- Moody's model heritage means the methodology withstands scrutiny from bank examiners and external auditors without custom model validation work
- Excel connector lets analysts run scenarios directly from existing credit templates without rebuilding data pipelines
Cons
- Pricing is quote-only with no published entry point — you cannot evaluate cost without a sales conversation, which makes budget comparison with competing tools like S&P Capital IQ or Sageworks difficult
- Model accuracy degrades for companies with fewer than three years of financial history or in industries with thin Moody's training data, such as early-stage or highly localized sectors
- Full portfolio analytics require the broader Moody's Analytics Platform subscription, so the sticker price understates total cost of ownership for multi-user teams
- Excel integration is functional but not native — bulk processing or automated refresh requires IT involvement or the platform tier
- Implementation and onboarding typically runs weeks, not days, particularly when connecting to internal loan origination systems
- No free trial or sandbox environment — you are committing based on demos, which limits hands-on evaluation before contract
Ledger Brief Take
This is Moody's institutional-grade credit modeling wrapped for practitioners who need bank-level risk assessment but lack the resources to build proprietary models. Built specifically for evaluating private companies where public market signals don't exist, it fills a real gap between basic ratio analysis and full credit committee infrastructure. The quantitative rigor here is genuine Moody's DNA, not a rebranded consumer tool.
Frequently Asked Questions
Common questions accountants ask about RiskCalc (Moody's).
How much does RiskCalc cost?
Moody's does not publish pricing. RiskCalc is sold through direct sales with custom contracts based on number of users, deal volume, and whether you need the standalone module or the full Moody's Analytics Platform. Expect enterprise-tier pricing — budget conversations typically start in the five-figure annual range.
Does RiskCalc integrate with QuickBooks or Xero?
No direct integration with QuickBooks or Xero. Financial data is entered manually, imported via Excel, or fed through the Moody's Analytics Platform via API. Small firms expecting a direct accounting software sync will need to build that bridge themselves or export financials to Excel first.
How does RiskCalc compare to Sageworks or S&P Capital IQ for private company credit risk?
Sageworks targets community banks with simpler workflows and lower cost. S&P Capital IQ skews toward public markets and M&A. RiskCalc is the strongest option when you need Moody's-calibrated default probability scores for unrated private companies and a methodology that holds up under regulatory examination.
Is RiskCalc appropriate for a small credit union or regional bank?
Only if you underwrite private company credits at volume. For fewer than 20 deals a year, the contract cost and onboarding overhead likely outweigh the benefit. Community lenders with lighter deal flow typically find Sageworks or a well-structured internal scorecard more cost-effective.
How does RiskCalc handle data security and confidentiality of borrower financials?
Moody's operates under enterprise data security standards with SOC 2 controls. Borrower financial data submitted to RiskCalc is not used to retrain public models. For specific data residency or NDA requirements, these terms are negotiated in the sales contract — confirm in writing before signing.
What financial data does RiskCalc actually require to generate a score?
At minimum, RiskCalc needs a balance sheet and income statement, typically covering two to three years. The more complete the data — including cash flow statements — the more reliable the output. Gaps in historical financials reduce model accuracy and widen the confidence interval on the probability of default score.