Current Expected Credit Losses (CECL) requires banks to combine historical experience, current conditions, and reasonable and supportable forecasts when estimating expected credit losses. Naturally then, the CECL solutions market has developed around different parts of that process

 

Within the first category are various credit risk data sources that serve as independent reference points for the assumptions behind the estimate. 

Traditional sources work well where their coverage conditions are met. Agency ratings provide established benchmarks for rated issuers; model-derived scores can extend coverage where sufficient borrower data is available; and market measures provide another signal where traded instruments exist.

The challenge becomes harder for unrated commercial borrowers, particularly where market coverage is absent and internal default experience is too sparse to provide a strong benchmark. That gap often becomes more visible during CECL model validation, audit or supervisory review, when independent data is needed to support and validate their assumptions. 

So, rather than a random list of tools, we will map the CECL solution stack and show where each category fits. We will pay particular attention to the external data gap for unrated commercial borrowers and how Credit Benchmark addresses it. We will then compare the other solution categories and show how banks can identify which part of their CECL process needs stronger support.

Note: To avoid duplication, we will profile providers once in the category that best represents their primary role in this guide. Where a provider spans other parts of the CECL stack, we will briefly mention those capabilities in the new section.

1. Credit Risk Data and Benchmarking

Credit risk data and benchmarking solutions provide external evidence for the allowance assumptions. Depending on the methodology, banks may use them to inform PDs and other credit estimates, calibrate models, compare internal ratings with external views, or challenge whether existing assumptions remain reasonable.

Four types of sources meet these needs, each built on different requirements, requiring a specific data source, and having a specific gap in their coverage which makes them complementary:

Let’s go through the four subcategories in more detail, along with the data source that closes the gap created by unrated commercial borrowers, private companies, subsidiaries, and middle-market entities.

i. Consortium and Peer-Sourced Credit Data

Consortium data works differently from most external credit sources. Instead of relying on public filings, agency coverage or traded instruments, it draws on information that banks already produce through their lending relationships. Participating institutions contribute internal credit assessments or loss data under agreed standards. A third party applies quality controls, anonymizes the submissions and combines them into a shared dataset or consensus view.

That makes consortium data particularly useful for unrated commercial and middle-market borrowers, where other external reference points may be limited.

Although Global Credit Data and Credit Benchmark both use bank-contributed data, they are not interchangeable. Global Credit Data pools historical default and loss experience, making it a source for historical loss evidence. While Credit Benchmark pools current internal credit assessments to produce consensus credit views and PD benchmarks, providing a current peer view of credit risk.

a. Credit Benchmark

CECL requires banks to produce a reasonable, supportable, and well-governed lifetime expected-loss estimate, but it gives institutions considerable flexibility in how they get there.

For banks that rely on internal ratings and PD-based methodologies, one recurring challenge is independently assessing whether those credit-risk assumptions remain reasonable. This is especially difficult for private, unrated, and low-default commercial portfolios, where agency coverage is limited, internal default history may be too sparse to validate against, and other external references may not offer current, borrower-level information.

Credit Benchmark provides a complementary external reference point. Its consensus PD benchmarks are derived from the internal credit assessments of more than 40 contributing financial institutions, covering a broad universe of entities that extends well beyond the traditional agency-rated population. 

Banks can compare their own borrower-level PDs against this consensus view and investigate material differences in calibration, rating philosophy, overrides, or borrower-specific information. For peer benchmarking, the bank’s own contribution can be excluded so the comparison remains fully independent.

Independent testing demonstrates that the consensus ratings carry strong discriminatory power, with an average one-year Gini of 0.88 over ten years compared with 0.91 for S&P across the same population. In practice, that gives banks an external benchmark with broad coverage and without a material trade-off in predictive performance.

This makes Credit Benchmark a source of objective external credit risk evidence that can strengthen multiple stages of the CECL model lifecycle. During model development or recalibration, consensus PDs provide external support for internal ratings. 

During validation, credit teams can use them to assess whether internal estimates are reasonable, particularly for low-default segments. And for CECL methodologies based on lifetime PDs, Credit Benchmark’s transition matrices show how borrowers migrate between credit grades over time, helping teams build and validate multi-year PD curves used in lifetime expected-loss calculations.

According to Chief Credit Officer Dale Clayton of KeyBank, the data has been particularly useful for unrated names and has directly informed model recalibration.

It’s worth clarifying that Credit Benchmark is not itself a CECL calculation tool; it provides the independent credit-risk evidence that banks use to support and challenge their own models and assumptions.

Beyond its core consensus PD data, Credit Benchmark offers datasets and analytics that can support other parts of the CECL process:

Credit Rating Transition Matrices show how entities move between credit grades over time. For banks using a PD/LGD approach, these can support developing lifetime PD term structures rather than relying only on a current 12-month PD.

Consensus Analytics reveals what sits behind the consensus estimate, including how widely contributing banks’ views are dispersed and whether those views are converging or diverging. This helps a bank judge how much agreement exists behind a benchmark rather than relying only on the average PD.

PSI analytics helps identify changes in the distribution of credit-risk measures over time. Banks can use this as an additional model-monitoring indicator between full validation cycles, particularly where limited defaults make deterioration harder to detect from internal outcomes alone.

Credit Indices summarize the direction of credit quality across a sector or region. They can serve as a high-level sanity check on whether a portfolio’s assumptions align with broader shifts in credit conditions.

Here’s an example of how banks combine these capabilities to source external benchmarks for unrated borrowers and strengthen their internal model calibration.

Case Study: How a bank uses Credit Benchmark

A super-regional US bank with over $50 billion in assets and a Credit Benchmark customer since 2017 uses consensus data to benchmark and recalibrate its internal credit models. Its credit team wanted an external reference for its internal probability of default (PD) and loss given default (LGD) estimates, particularly for unrated borrowers where agency ratings were unavailable. So, they incorporated consensus data into their modelling process in three ways:

• Peer benchmarking: The team compares its internal credit views with those of other banks, including ahead of Shared National Credit (SNC) exams.
• Model recalibration: Peer data provides an additional reference when the team reviews and refines its PD and LGD estimates.
• Unrated borrower validation: For names without an agency rating, consensus data provides an external benchmark that would otherwise be difficult to obtain.

The additional reference point increases the team’s confidence in its model outputs and gives it a clearer baseline for comparing internal credit assessments with peer views.

b. Global Credit Data

Global Credit Data (GCD) is a non-profit consortium owned and governed by its member banks. Members contribute historical default, loss given default (LGD) and exposure at default (EAD) data from their own portfolios. GCD then combines those observations into a pooled dataset that gives each member access to a much larger history of credit losses than its internal data.

Its main value is calibration and benchmarking. A bank may have relatively few defaults in a particular portfolio, which limits the amount of internal evidence available to test LGD or EAD assumptions. GCD provides a broader sample based on the actual default and recovery experience of participating banks.

This makes GCD useful as a source of pooled historical credit-performance data. But it is not suited for showing how other banks assess a specific borrower’s credit risk today.

ii. Agency Ratings

Agency ratings are often the first external reference point available to a bank. For a rated borrower, they provide an independent view of credit quality that can be compared with the bank’s internal rating.

Their historical datasets add a second use. Rating agencies publish default and transition studies that show how borrowers in different rating grades have performed over time. Banks can use those histories to test rating mappings, compare migration behaviour, and assess whether internal risk differentiation broadly aligns with external views on the same borrowers.

Before we get to their shared limitation, here are the top agency rating providers:

a. S&P Global Ratings

S&P Global Ratings provides issuer and issue ratings, along with long-running default and transition studies.

CECL or model-risk teams use its current rating to get an independent view of the borrower’s credit quality, and the historical performance data to track how borrowers in comparable rating categories have migrated and defaulted over time. This can support the review of internal rating mappings and provide an external reference for rated borrowers. 

S&P also makes clear that a rating should not be treated as a specific PD, so converting an S&P grade to an internal PD requires a separate mapping or calibration step. For an unrated borrower, however, there is no S&P issuer rating to use as a single-name comparison.

b. Fitch Ratings

Fitch Ratings provides issuer and instrument ratings, supported by credit research, transition data and default studies. Banks can use these ratings as an external comparison for internal credit grades and draw on Fitch’s historical data when assessing how different rating categories have performed over time.

Fitch also provides several forms of credit assessment beyond a standard public rating:

• Indicative Ratings give first-time issuers an initial view of the rating they might receive before entering the full rating process.
• Rating Assessment Services assess how a proposed transaction, event or scenario could affect credit quality, including cases where Fitch does not already rate the entity.
• Credit Opinions provide a Fitch credit view but do not include all the features or processes associated with a full credit rating.

These services can extend Fitch’s usefulness beyond its published ratings universe. However, they still require a specific Fitch assessment. For borrowers with no Fitch rating, opinion or assessment, banks need another source of external credit evidence.

c. Moody’s Ratings

Moody’s Ratings provides issuer and instrument ratings, along with extensive historical research on defaults, recoveries, and rating transitions.

For banks, the ratings provide an independent credit-quality benchmark, while the historical studies can support analysis of how rating categories have performed through time. This makes Moody’s useful both for comparing individual rated borrowers and for broader calibration or validation work.

Moody’s also offers quantitative PD models and economic scenarios through other parts of the business. But like other agency rating providers, banks still need another source if a commercial borrower does not have a Moody’s rating.

Agency ratings have two practical limitations in CECL benchmarking.

First, a rating is not the same as a probability of default (PD). It is an ordinal view of credit quality, so a bank may need to map the agency grade to an internal rating scale or historical default rate before it can compare it with a PD-based CECL model.

Second, agency coverage stops where no rating exists. For many private, middle-market and other unrated commercial borrowers, there is simply no agency view to compare with the bank’s internal assessment.

This is why Credit Benchmark serves as a complementary credit risk source that gives banks an external peer reference for names that may sit outside the traditional agency universe.

iii. Model-Derived Credit Scores

Model-derived credit scores are useful when a borrower has no agency rating, but enough financial or market data exists to estimate its credit risk. A vendor model takes those inputs and produces a score, rating or probability of default (PD) that the bank can compare with its own internal estimate. For example, if a bank assigns a 1.2% PD to a private company, a model-derived PD gives the team another estimate against which to test that assumption.

Here are the top providers of model-derived credit scores:

a. Bloomberg DRSK

Bloomberg DRSK provides a one-year probability of default, along with estimates for other time horizons, for both public and private companies. This helps banks compare their internal view with an external quantitative measure.

Its main advantage is breadth and timeliness. Bloomberg reports coverage of more than 40,000 public companies and around 300,000 private companies globally. The model also reflects current market conditions and shows the factors driving changes in default probability, helping teams understand why an external estimate has changed.

However, like other model-derived tools, the output depends on Bloomberg’s methodology and the quality of available inputs.

b. RapidRatings

RapidRatings focuses more heavily on company financial statements. Its Financial Health Rating uses information from the balance sheet, income statement and cash-flow statement to assess financial strength and produce credit-risk measures for private and public entities in over 140 countries.

For CECL teams, RapidRatings can therefore provide another reference point when reviewing an internally assigned credit grade or PD.

The trade-off is the data requirement. The analysis depends on having sufficiently complete and current company financials. It can extend beyond the agency-rated universe, but it cannot solve a coverage problem when the necessary borrower information is unavailable.

Other options include Moody’s RiskCalc and CreditEdge EDF, as well as S&P Global Market Intelligence Credit Analytics. RiskCalc uses company financial information to estimate private-company credit risk, while CreditEdge incorporates market-based information for public companies. S&P also provides quantitative credit models for rated and unrated companies.

These tools give banks another external PD or credit score to compare with their own estimate. But the result still reflects that provider’s model and the borrower data it uses, rather than other lenders’ independent views.

iv. Market-Implied Credit Measures

Market-implied credit measures use prices from traded debt markets to show how investors are pricing a borrower’s credit risk. The most common examples are credit default swap (CDS) spreads and corporate bond spreads.

Their main advantage is speed. If investors become more concerned about a borrower, CDS or bond spreads can widen quickly. A bank can use that movement as an external check on whether its own credit assessment still reflects current market conditions.

The main limitation is coverage. Market-based measures require an observable traded instrument, so they work best for public issuers and are often unavailable for private borrowers. Spreads also reflect liquidity and market stress as well as credit risk, so banks use them as a supporting signal rather than a direct substitute for an internal PD.

a. ICE

ICE provides pricing, market data and analytics across fixed-income and derivatives markets. For credit-risk teams, the most relevant products include evaluated bond prices, issuer and sector bond curves, aggregated market data and credit-derivatives valuations.

These datasets can provide an external market reference for borrowers with traded debt. For example, a bank can use changes in bond spreads or issuer curves to see whether the market is pricing a deterioration or improvement in credit quality that is not yet reflected in its internal assessment.

ICE also provides continuous and end-of-day evaluated pricing, which can help where individual bonds trade infrequently and a directly observed market price is not always available.

The limitation is still coverage at the borrower level. These signals depend on the borrower having relevant traded instruments or market data. A private commercial borrower with no public debt or credit derivatives will not have the same single-name market benchmark.

b. SOLVE

SOLVE provides market pricing and quote data across corporate bonds, syndicated loans, credit default swaps (CDS) and other fixed-income instruments. It processes more than 30 million quotes each day and converts fragmented dealer messages into standardized data that can be compared across issuers and securities.

For credit-risk teams, this provides a current external view of how the market is pricing a borrower or comparable credits. A bank can track changes in bids, offers and spreads, compare the borrower with similar issuers, and review historical quote data to see whether market perceptions have changed over time. SOLVE also covers syndicated loans, which can extend market-based evidence beyond borrowers with publicly traded bonds.

The limitation is still coverage. SOLVE needs observable market activity. If a private borrower has no traded debt, CDS or quoted syndicated loan, there is no single-name market signal to use. And even where pricing exists, it is market evidence rather than a direct PD benchmark.

2. CECL Calculation Software

Calculation software runs the CECL process. It takes the bank’s chosen methodology (such as WARM, vintage analysis, discounted cash flow or PD/LGD) and turns portfolio data into an allowance, with the workflow, adjustments, controls and reporting around it. Banks usually look at this category when the existing process has become difficult to manage.

It’s important to note that calculation software only processes the assumptions it receives; it will not create a PD for an unrated borrower or supply the economic forecast. If the real problem is missing data or weak external support, a better calculation platform will not solve it.

Here are the top providers:

a. Abrigo

Abrigo Allowance is a purpose-built CECL platform for banks and credit unions. It automates allowance calculations, documentation and reporting, and supports methodologies including migration analysis, vintage analysis, PD/LGD, transition matrix, static pool, remaining life and discounted cash flow. Institutions can therefore use different approaches across portfolios without running separate calculation processes.

Its main strength is operational control around the allowance. Abrigo can integrate with core systems, archive portfolio data, generate pre-built disclosures and maintain a repeatable process from one reporting period to the next. That makes it particularly relevant for institutions moving away from spreadsheet-based CECL workflows or trying to improve auditability.

Abrigo also includes peer loss benchmarking based on proprietary data from thousands of institutions, which can supplement limited internal loss history. This is useful for calibration and model monitoring, but it differs from a single-name peer PD benchmark because it doesn’t show how other banks currently assess a specific borrower.

b. SS&C EVOLV

SS&C EVOLV Reserving is an end-to-end reserving platform that brings the main CECL steps into one controlled workflow. It prepares and tracks source data, runs reserving models, applies qualitative adjustments, and produces allowance reporting and disclosures. The platform also maintains data lineage and built-in controls for data integrity and segregation of duties.

A key strength is model flexibility. EVOLV can run SS&C models, third-party models, or models developed by the bank itself, with different approaches used across different portfolio segments. It also supports macroeconomic scenarios and challenger models within the same reserving process.

This makes EVOLV particularly relevant to institutions that want to connect risk and finance within a governed CECL workflow, rather than simply automate a standalone calculation. 

Like other platforms, it can only process the models and assumptions supplied to it, but it does not by itself provide an independent borrower-level benchmark where external PD coverage is missing.

c. SAS

SAS provides CECL through a broader enterprise risk-management environment. Its Expected Credit Loss solution combines data management, model execution, scenario management, workflow and reporting, with controls over approvals and changes throughout the process.

The platform is most relevant to institutions that need more than a standalone allowance calculator. Banks can use the wider SAS environment for risk modeling, model-risk management and stress testing, as well as CECL. This keeps data, models, and governance processes within a common infrastructure.

Additionally, a bank already using SAS for enterprise risk analytics can extend the same environment into CECL rather than introduce a separate point solution. But for a smaller institution that only needs to automate its allowance calculation, the breadth of an enterprise platform may be more than the immediate problem requires.

3. Macroeconomic Scenario Providers

Macroeconomic scenario providers supply the forward-looking economic assumptions that banks use in their CECL estimates. Typical variables include GDP, unemployment, interest rates, inflation and property prices.

Banks usually turn to these providers when they do not want to build and maintain the full economic forecasting process internally, or when they need an independent forecast to compare against their own assumptions. 

These providers only describe the economic environment the credit model should operate under; the bank still needs models and credit-risk inputs that translate those conditions into expected losses.

a. Oxford Economics

Oxford Economics offers a CECL-specific scenario service for banks that need an external set of reasonable and supportable economic assumptions. It provides baseline, upside, and downside scenarios, assigns probabilities to each, and updates them quarterly with documentation explaining how the scenarios were built and why the probabilities changed.

The service is built on Oxford Economics’ Global Economic Model and combines national, state and metropolitan forecasts within one consistent framework. This helps banks apply a common economic view across portfolios while still reflecting regional differences.

Oxford also includes a Severe Downside scenario designed to be broadly comparable in severity to the Federal Reserve’s Severe Adverse CCAR scenario. That gives risk teams an additional external reference when assessing downside conditions.

b. Moody’s Analytics

Moody’s provides economic scenarios specifically for CECL alongside its broader forecasting and credit-risk products. Its economic database covers 100+ countries and jurisdictions and more than 12,000 variables, with scenarios updated monthly to incorporate new economic data and expectations.

Banks can use Moody’s baseline and stressed scenarios as reasonable-and-supportable economic assumptions, or develop custom scenarios for specific portfolios and risk questions. Moody’s also provides tools for scenario weighting and for connecting economic conditions to credit-loss models.

The main distinction from Oxford Economics is breadth of offering. Moody’s scenarios are part of a broader Moody product set that also includes credit data, models, an impairment calculation engine, and reporting.

c. S&P Global Market Intelligence

S&P Global Market Intelligence also provides economic forecasts and scenario-based credit analytics. Its Macro-Scenario Model links changes in macroeconomic conditions to changes in company credit risk and can produce stressed PD term structures under different economic assumptions. S&P positions the tool for applications that include CECL and IFRS 9 expected-credit-loss analysis.

That makes S&P slightly different from a pure scenario provider. It doesn’t just supply an economic outlook; its tools can also show what a particular scenario could mean for counterparty credit risk.

4. CECL Consulting and Model Validation

Consulting and validation firms assess whether the bank’s CECL methodology, models, assumptions, implementation, controls, and documentation are fit for purpose. They are valuable for independent challenge or specialist support during scheduled model validation, a material model change, an internal-audit finding, or remediation work after supervisory review.

These firms can only identify a weakness. If they find that an internal PD lacks sufficient external support, for example, the bank may still need a separate benchmarking dataset to address the finding.

a. PWC

PwC supports the full model lifecycle, from model development and implementation through validation, ongoing monitoring and governance. For a bank reviewing its CECL framework, the most relevant services are independent model validation and model risk management. 

PwC can assess model design, implementation, governance, and monitoring, and conduct gap assessments against regulatory expectations and industry practice. Where it identifies weaknesses, it can help redesign model risk processes or support remediation.

PwC also supports internal audit teams with model-specific audits and reviews of the broader model risk management framework. This makes it most relevant when the bank needs an independent assessment of whether its models and controls are working as intended.

b. Crowe

Crowe has a dedicated CECL model-validation offering for banks and other financial institutions. Its work tests both the development and operation of CECL models, aiming to identify weaknesses that could affect loss estimates. Crowe also uses its Credit360 for CECL framework to support model testing and challenge.

This makes Crowe particularly relevant when the requirement focuses on CECL validation rather than broader enterprise transformation. Its validation work can identify issues in methodology, assumptions, implementation, controls, or model performance.

c. EY

EY provides accounting, credit-risk, and model-risk advisory services that can support CECL methodology and validation. Its broader financial-services capabilities also make it relevant where CECL is part of a larger finance or risk transformation.

A bank might therefore use EY where the problem extends beyond a single model and involves governance, process design, accounting interpretation, or changes to the wider risk infrastructure.

As with the other firm in this category, however, identifying a data problem does not itself solve it. If validation shows that an estimate cannot be adequately benchmarked because internal defaults are sparse or external coverage is missing, the bank still needs an external data source.

Which Types of CECL Solutions Does Your Bank Need?

At the leadership level of the credit teams in banking institutions, the decision is less about product features than about which risk, control, or evidence gap the institution needs to address. The four questions below usually point to the right category.

 

The diagnosis matters because each category solves a different problem. A stronger calculation platform will not resolve an unsupported PD. Better external credit data will not fix a weak close process. A scenario provider can strengthen the economic assumptions, but it will not validate the methodology used to translate those assumptions into expected losses.

Banks may therefore use several categories at once. A calculation platform can run the allowance, an external scenario provider can supply the economic outlook, credit-risk data can provide independent benchmarks for key assumptions, and a validation firm can challenge the framework.

For unrated commercial borrowers, the decision often comes down to the first question. If the bank already has a functioning CECL engine and methodology but lacks an external reference for its internal PD, replacing the calculation software will not close the gap. The relevant need is credit risk data and benchmarking. If the remaining gap is external PD support for unrated commercial borrowers, the next step is to see how much of the portfolio Credit Benchmark can cover.