8 Best Credit Data Providers for Insurance Risk Management (2026)
Posted by Laura Saville on October 22, 2026
An insurer’s credit risk extends beyond its investments. A borrower may struggle to repay a private loan, a contractor may fail to meet obligations backed by a surety bond, an insured buyer may default on a trade debt, or a reinsurer may become less able to pay claims. Each situation raises a similar question: how strong is the company behind the exposure, and what happens if its credit quality weakens?
The answer can be difficult to answer when the company has no public rating. Underwriters and investment teams may have enough information to make an initial decision, but they also need to know whether that assessment still holds months or years later.
Credit data helps teams test their internal judgments and identify exposures needing another review. A life insurer can check the credit assumptions behind a private debt investment. A surety team can track a contractor’s credit quality over the bond term, and a Lloyd’s credit and political risk underwriter can monitor an insured buyer after the policy begins. Reinsurance teams need the same view of the counterparties expected to pay future claims.
However, regulators in each major market expect insurers to evidence these judgments:
- In the US, the NAIC has required supporting reports explaining private letter ratings since 2022 and is now developing a framework to assess rating methodologies and mappings. These measures give regulators more evidence to examine the credit risk behind insurers’ private investments.
- Under Solvency UK, insurers using internal models (including UK specialty carriers outside Lloyd’s) must test the credit assumptions used to calculate capital and oversee model changes. PRA SS17/16 addresses credit risk assessment, validation and board oversight. For UK life insurers holding illiquid, unrated matching adjustment assets, SS3/17 adds expectations for documenting and validating internal credit assessments. The PRA’s 2026 insurance supervision priorities letter (January 15, 2026) tells insurers to “pay particular attention to exposures to private credit assets” and flags counterparty risk in funded reinsurance. The Bank of England is also running a system-wide exploratory scenario on private markets this year.
- At Lloyd’s, managing agents must maintain effective risk oversight and support decisions with appropriate data. Lloyd’s internal model guidance also calls for validation, including stress and scenario testing.
- The Bermuda Solvency Capital Requirement (BSCR) generally applies higher capital charges to lower-rated fixed-income assets and includes a separate charge for concentration risk. The BMA has also strengthened oversight of private credit and investment risks in life insurance and asset-intensive reinsurance. Its Insurance Code of Conduct calls for counterparty exposure limits, monitoring credit-risk trends, and stress and scenario testing.
None of these frameworks requires a particular data source. They do expect insurers to show how they reached a credit view, how they tested it, and how they keep it current.
Agency ratings and model-based scores meet part of that need. However, neither offers an independent view of the private and unrated obligors that make up a growing share of insurers’ exposures. Agency ratings mostly cover companies that have sought a rating. Where insurers rely on private letter ratings instead, the rating itself may overstate credit quality. A Columbia Business School study reported by Bloomberg in June 2026 found that private ratings are more inflated than public ones, and that privately rated bonds were twice as likely to become impaired. Model-based scores often rely on the same borrower financials the insurer already uses, or on market prices that private companies don’t have. Even where coverage exists, few sources update often enough to track an exposure between formal reviews.
Bank consensus data helps close that gap. It extends independent coverage to private and unrated names the other two sources miss, gives teams an outside view to explain and defend their own assessments, and updates weekly over the life of an exposure.
To close that gap, insurers combine tools from separate categories. We’ve placed independent credit intelligence first in the table because it addresses the unrated gap:
| Category | What it provides | Role for insurers |
|---|---|---|
| Independent credit intelligence and benchmarking | Credit views drawn from other institutions’ assessments | Benchmarks internal ratings and monitors private and unrated obligors throughout an exposure’s life |
| Rating agencies | Analyst ratings of issuers, instruments and insurers | Supports investment approvals, credit reviews and reinsurer assessment |
| Model-derived and market-implied scores | Risk estimates from financials or market prices | Adds quantitative signals on listed companies |
| Complementary analytics (platforms and in-house) | Tools that combine credit data with exposures | Supports portfolio monitoring and management and regulatory reporting |
This guide compares six credit data providers across three categories, plus two complementary analytics approaches. It covers what to check on each, including entity-level coverage, methodology, independent validation, ongoing surveillance and integration.
Unrated exposures can leave insurers with limited external evidence to test their credit assessments. See how bank consensus data can help close coverage gaps and track changes in borrower credit quality.
Credit Data Solutions for Insurers Compared
| S/N | Category | Provider or approach | Best for |
|---|---|---|---|
| 1 | Independent credit intelligence and benchmarking | Credit Benchmark | Insurers and reinsurers that need to benchmark, explain, and defend their credit assessments of private, unrated, and thinly covered obligors, counterparties, and holdings. |
| 2 | Rating agencies | S&P Global Ratings | Issuer and instrument ratings, outlooks and CreditWatch monitoring |
| Moody’s Ratings | Private monitored ratings and assessments of structured-finance transactions | ||
| Fitch Ratings | Analyst ratings for private debt, recovery analysis and Insurer Financial Strength ratings | ||
| 3 | Model-derived and market-implied scores | Bloomberg DRSK | Adding model-based default-risk signals to Bloomberg workflows |
| Moody’s Analytics CreditEdge | Monitoring public-company default risk using EDF measures | ||
| 4 | Complementary analytics | GoodData | Building dashboards and reports from existing credit and exposure data |
| Building in-house | Tailoring monitoring and reporting to internal data, systems and governance |
1. Independent Credit Intelligence and Benchmarking
Independent credit intelligence lets insurers compare their own assessments with those of other institutions. Bank consensus data combines several banks’ views of the same borrower or counterparty, including companies without public ratings. Coverage depends on the exposures contributing banks assess, so the dataset does not include every name.
The comparison matters at two points. At inception, a gap between the insurer’s view and the banks’ view shows analysts where to dig before they commit capacity or capital. After inception, changes in bank assessments flag obligors that need another review, often before an agency acts.
This evidence complements agency ratings and internal research. Analysts still need to consider the investment’s terms and protections, including likely recovery if the borrower defaults.
a. Credit Benchmark
Best for: Insurers and reinsurers that need to benchmark, explain, and defend their credit assessments of private, unrated, and thinly covered obligors, counterparties, and holdings.
Credit Benchmark combines the internal credit assessments of more than 40 contributing banks into consensus data covering over 125,000 entities. Approximately 90% have no traditional agency rating.
Banks contribute one-year probabilities of default (PDs) for entities they have exposure to. Then, Credit Benchmark validates and anonymises these submissions, and publishes consensus PDs and Credit Consensus Ratings weekly. This gives insurers an independent benchmark drawn from banks managing their own credit risk.
Insurers use consensus data at three points in their credit risk workflow: to benchmark their own ratings, to understand what drives the bank view, and to track each exposure after the initial decision.
Teams start by matching investments, insured obligors, and counterparties using company names and identifiers such as LEIs. The match identifies the legal entity behind each exposure, which may be a subsidiary rather than its parent, and shows where coverage is missing.
Once matched, investment, underwriting, and risk teams can compare their internal ratings with the consensus. Where there’s a divergence, analysts can revise their assessment of the obligor, unless the difference is due to other factors such as information the banks may not hold, the terms of the specific exposure, or differences in rating scales and default horizons.
Credit Benchmark provides context for that comparison. Analysts can see how many banks assess a company and how closely their views agree. Wide disagreement may deserve a closer look. Opinion-change indicators help distinguish banks changing their credit judgments from changes in which banks contribute to the consensus.
This gives the risk or credit committee more than a rating to consider. Analysts can explain what differs from their internal view and record why they retained or revised it.
The same evidence remains useful after an investment closes or a policy begins. Weekly consensus updates provide early warning signals that can prompt a review before the next scheduled assessment. If banks see a borrower as riskier, analysts can check its latest financial information and raise significant concerns with the risk team. The findings can then guide future underwriting and exposure decisions, while respecting existing contracts.
How teams use consensus data depends on the exposure they manage:
| Insurance segment | Key credit challenge | How Credit Benchmark can help |
|---|---|---|
| Lloyd’s and UK specialty credit and political risk | Assessing private or unrated obligors and following changes after a policy begins. | Adds an independent underwriting benchmark, weekly monitoring and evidence for management reporting. |
| Surety | Assessing contractors and commercial principals throughout the bond term. | Supplements financial analysis with consensus ratings, PDs and credit migration history. |
| Reinsurance | Monitoring reinsurer counterparties and concentrations in recoverables. | Adds an external credit view to counterparty reviews and ongoing exposure monitoring. |
| UK life insurance | Assessing private debt and testing credit assumptions in long-term portfolios. | Provides internal rating comparisons and PD history to support credit analysis and validation. |
| US insurance | Supporting internal assessments of private credit investments under regulatory scrutiny. | Adds evidence for investment decisions and helps teams explain differences between internal and external views. |
| Bermuda life and asset-intensive reinsurance | Understanding the credit quality and concentrations of assets supporting long-term obligations. | Adds borrower-level credit evidence and monitoring inputs for portfolio reviews and stress analysis. |
None of this requires a new system. Teams access the data through platforms they already use, including Bloomberg, Snowflake, AWS, and Databricks, as well as a web application, Excel add-in, API, and file feeds. Consensus views can then sit alongside internal ratings in existing dashboards and reports.
Keep in mind that Credit Benchmark complements agency ratings and internal analysis. It strengthens the insurer’s credit process with an external reference teams can use across investments, underwriting, and counterparty reviews.
How a Lloyd’s Specialty Insurer Uses Credit Benchmark
A Lloyd’s specialty insurer’s credit and political risk team insures the payment and performance risk of companies around the world, many of them private or unrated. Its exposure to each one runs for the full policy term. The team also has to meet Lloyd’s requirements for internal modeling and stress testing, and show management how it reached and maintains its credit views.
Credit Benchmark is now a core tool in that work, supporting the team across the life of each risk:
- More confident underwriting. Entity mapping connects insured borrowers to consensus ratings and PDs. Underwriters can compare their assessment with banks’ views before committing capacity. Access to this additional evidence has helped the insurer expand underwriting activity.
- Ongoing credit monitoring. Weekly updates help the team identify borrowers whose credit quality may be weakening. Those changes can prompt a closer review, with findings referred through the insurer’s risk process and considered in future underwriting decisions.
- Stronger risk governance. Consensus data supports management reporting, internal modeling and stress testing. It gives the team an external reference to explain its assessments and investigate differences between its own judgment and the banks’ view.
The insurer now has an external credit reference it can return to throughout the policy term. This helps the team revisit its underwriting judgments as conditions change and explain its decisions to management.
“Credit Benchmark has been successfully embedded as a core tool in our first year — central to how we meet our regulator’s mandate for internal modelling and stress testing.”
Lloyd’s Specialty Insurer, Credit & Political Risk
A global specialty insurer’s credit risk team reports similar gains at the underwriting stage:
“Credit Benchmark has strengthened both the quality and speed of our underwriting decisions. The independent consensus view gives us greater confidence where public ratings are limited, helping us assess counterparties more efficiently, approve opportunities more quickly, and support larger transactions that might otherwise require additional review.”
Head of Credit & Portfolio Risk Management, Global Specialty Insurer
Book a coverage assessment to see how many of your insured obligors, counterparties and holdings Credit Benchmark covers, and how bank consensus data can support underwriting, monitoring and governance across your book.
2. Rating Agencies
Rating agencies assess an issuer’s ability to repay debt or the credit risk of a particular instrument. Insurers use this data, along with supporting research, to set investment approvals, exposure limits, and ongoing credit reviews.
a. S&P Global Ratings
Best for: Insurers seeking issuer and instrument ratings, supporting research and ongoing monitoring of rated fixed-income holdings.
S&P Global Ratings assesses both borrowers and individual debt instruments. Beyond the rating itself, insurers can use S&P’s research to understand the assessment and monitor potential changes. Rating outlooks indicate the possible direction of a rating over the next six to 24 months, while CreditWatch highlights developments that could lead to a nearer-term change.
These signals can help investment teams identify holdings that need closer review.
For integration into existing systems, RatingsXpress provides S&P Global Ratings’ issuer and issue ratings, outlooks, CreditWatch assessments and associated dates. Its coverage spans corporate and financial issuers, sovereigns, public finance and structured finance.
Insurers should check coverage against their actual holdings. Where the relevant borrower or instrument has no S&P rating, internal research or another credit data source will still be needed.
b. Moody’s Ratings
Best for: Insurers seeking agency ratings and supporting analysis for rated investments, including private transactions.
Moody’s Ratings assesses credit risk across corporate debt, financial institutions, public-sector borrowers and structured finance. Analysts use published methodologies, and committees decide ratings. For investment teams, those methodologies help explain the factors behind a rating and provide a basis for comparison with their own assessment.
Its public ratings receive ongoing monitoring. Rating actions and outlooks help insurers track changes in credit quality and identify holdings that warrant further review.
Moody’s also offers confidential, monitored ratings to requesting investors. This gives insurers a way to obtain an agency assessment without publicly disclosing the rating.
c. Fitch Ratings
Best for: Insurers seeking agency assessments of borrowers and debt instruments, including analysis of recovery prospects and insurance counterparties.
Fitch helps insurers assess both a borrower’s ability to repay and the risk of the specific debt they hold. Its Issuer Default Ratings address the borrower’s default risk, while instrument ratings also account for the debt’s ranking and expected recovery. This helps investment teams understand why a senior secured bond may carry a different rating from subordinated debt issued by the same company.
Fitch also publishes transition and default studies showing how ratings have changed and how often rated borrowers have defaulted. These include analysis of its Privately Monitored Ratings portfolio, giving teams historical evidence to consider when reviewing public and private credit exposures.
For insurers reviewing their reinsurance counterparties, Fitch offers Insurer Financial Strength ratings. These assess an insurer’s ability to meet policyholder obligations, including assumed reinsurance, and help determine whether a reinsurer can meet its commitments.
For all agency rating providers, coverage remains the practical limitation. Beyond the lack of ratings for private entities, a parent company’s rating may not reflect the subsidiary or instrument behind the insurer’s exposure.
Where a suitable rating is unavailable, internal research, consensus data or model-derived estimates can provide additional evidence.
3. Model-derived and market-implied credit scores
An insurer may want to know whether a borrower’s risk is rising before its next scheduled credit review. Model-derived scores help answer that question by estimating credit risk from financial statements, market prices, or both. Insurers use these estimates to flag holdings for closer review and compare risk across borrowers.
The major limitation of tools within this category is that a score can change because of market volatility or limited trading, rather than a change in the borrower’s ability to repay. Financial statements may also be out of date. Analysts need to understand what caused the change and compare it with other credit evidence before revising their assessment.
a. Bloomberg DRSK
Best for: Insurers using Bloomberg that want model-based default estimates to support company credit reviews.
Bloomberg’s Default Risk model estimates a company’s 12-month probability of default. It also estimates CDS spreads, including for companies without a traded CDS. This gives investment teams another measure of credit risk where a directly observable CDS spread is unavailable.
DRSK combines company fundamentals with market inputs to estimate default risk. For insurers, a rise in the estimate can prompt analysts to revisit a holding, examine the underlying changes and decide whether their internal assessment still holds.
b. Moody’s Analytics CreditEdge
Best for: Insurers seeking model-based default probabilities, peer comparisons and alerts to monitor listed-company exposures.
CreditEdge combines Moody’s Analytics’ Expected Default Frequency (EDF) measures with company and portfolio analytics. Its public-company model uses equity prices and financial information to estimate default probabilities, giving teams a quantitative measure to compare with internal assessments and agency ratings.
Teams can track portfolio movers, receive alerts, and compare companies with selected peers or industry groups. These features help analysts determine whether rising risk is specific to a borrower or part of a broader sector trend, then prioritise holdings that need closer review.
CreditEdge also offers historical EDF and loss-given-default data, stressed EDF measures and what-if analysis. Insurers can use these to examine changing credit assumptions and potential stress scenarios.
4. Complementary Analytics Approaches
GoodData and in-house analytics connect an insurer’s existing credit assessments to its exposures. Teams can see how much they have invested in a weakening borrower or how much cover they have written on the same company across policies. Bringing those positions together gives management a clearer picture of the risk.
a. GoodData
Best for: Insurers building customised analytics and shared reporting from existing data.
GoodData helps insurers turn their data into interactive dashboards. Its insurance offering focuses on underwriting and claims, with tools to detect anomalies and automate alerts. Users can drill into reported figures to investigate the underlying patterns.
Its role in credit monitoring depends on the data an insurer supplies. Once credit assessments are connected to holdings, teams could build views showing exposure by rating or concentrations in borrowers whose credit quality is weakening. These would be configured applications, not ready-made credit assessments.
GoodData supports connections to existing data sources, with FlexConnect available for custom connections. Its APIs and SDKs also let insurers embed analytics in other applications.
b. In-house analytics and reporting
Best for: Insurers with specific reporting needs and the technical resources to maintain their own analytics.
An insurer can combine a data warehouse with SQL or Python analysis and Power BI or Tableau dashboards. This lets teams build credit reporting around their own investment limits and incorporate proprietary information, such as their experience with a borrower.
The advantage is control. When a credit committee asks a new question, the team can adapt its analysis without waiting for a vendor to change its product.
But detailed reporting does not independently validate the underlying credit assessment. Reusing the same assumptions to produce and check an internal rating can leave weaknesses unchallenged. An external assessment gives analysts another view to examine, provided it covers the same borrower and uses a comparable methodology.
External credit data can feed these tools alongside internal ratings. Teams can investigate differences and retain the reasons behind their final judgment within the reporting process.
The insurer must also maintain those connections. Data feeds need monitoring, entity matches need review, and calculations need clear ownership. The decision to build internally therefore depends on whether the team can keep the system reliable as its portfolio grows.
How the Categories Work Together
Consider a hypothetical Lloyd’s syndicate asked to write a three-year non-payment policy for a commodity trader. The policy would protect the trader if a mid-sized buyer in an emerging market fails to pay.
The buyer has no public rating, traded debt or listed equity, its audited accounts are out of date, and there’s no private agency rating. However, several banks lend to the buyer and contribute their assessments to bank consensus data.
- Independent credit intelligence and benchmarking: Entity mapping helps connect the buyer named in the broker’s submission to its consensus rating and PD. The underwriter compares this with the syndicate’s internal assessment. If banks see greater risk, the team investigates before deciding on pricing, line size, and terms. After binding, weekly updates help identify changes needing review.
- Rating agencies: There is no agency rating available for this buyer. Sector and sovereign research can help explain the conditions affecting its business, but does not provide a direct borrower assessment. Agency ratings may also inform reviews of the syndicate’s reinsurers and banks involved in the transaction.
- Model-derived and market-implied scores: The lack of listed equity or traded debt limits market-based estimates. Financial-statement models may still be usable, but outdated accounts reduce their value as a current assessment.
- Complementary analytics: The reporting system links the policy to other exposures to the same buyer, country, and sector. It shows the potential loss size and keeps the review history available for management reporting.
Suppose the consensus weakens during the policy term. Analysts investigate the change and use the portfolio view to judge its significance. The syndicate’s risk process reviews material concerns and records findings for subsequent reviews and future underwriting decisions. Existing policy terms still govern what action is possible.
Here, bank consensus provides a direct external-borrower assessment where other sources leave a gap. Agency research adds context, while analytics connects the evidence to the syndicate’s book. The usefulness of that reporting depends on the credit information feeding it.
A life insurer reviewing an unrated private placement can use the same approach: compare its internal judgment with available external evidence before approval, then follow changes after the investment.
How to Choose a Credit Data Provider for Your Insurance Portfolio
Many factors matter when you choose a credit data provider, and the most important ones depend on the gap you need to fill. A private credit team may care most about borrower coverage. A credit and political risk or surety team may need multi-year surveillance. In either case, here are the criteria to consider:
- Entity-level coverage of actual exposures: The data should assess the specific legal entity behind each holding, policy, or counterparty, including subsidiaries, not just parent ratings or proxies. The size of uncovered exposures also matters. Missing one large exposure can outweigh coverage of many small positions.
- Independent validation: Evidence should come from outside the team that produced the internal view, so it can actually test that view. Ask where each assessment originates: agency analysts, models built on the same borrower financials, or lenders with their own exposure.
- Ongoing surveillance: Underwriting and counterparty exposures can run for years. Check how often the data updates, how much visibility it gives into the drivers of change, and its ability to alert teams between scheduled reviews.
- Comparable methodology: Rating scales and default horizons determine how meaningfully an external assessment can benchmark an internal view.
- History: Historical data helps analysts tell a persistent shift from a single observation.
- Support for management and regulatory reporting: Methodology documentation, dated assessments and exportable histories help teams explain decisions to credit committees, boards, auditors and supervisors.
- Total cost: Subscription fees are only part of the expense. Integration work, ongoing maintenance and licensing terms for cross-team use also affect value.
Strengthening confidence in insurance credit decisions
An insurer needs to explain why it trusts a credit assessment and recognize when that judgment needs another look. That applies to private debt investments as well as insured borrowers and reinsurance counterparties.
Different sources help answer different parts of the question. Agency ratings and model estimates add evidence, while analytics platforms connect assessments to exposures. Bank consensus gives teams another view to test against their own. Credit Benchmark’s bank consensus provides an external reference for internal assessments and ongoing monitoring, especially for unrated entities.
Book a demo to explore how bank consensus data can support underwriting, counterparty reviews, and investment monitoring.
Frequently asked questions
How can credit data support Lloyd’s insurers?
Credit data can help underwriters compare their assessment of an insured borrower with an outside view and follow changes after the policy begins. Its usefulness depends on the borrower match and how the signal relates to the policy’s coverage.
What credit data is useful when reviewing reinsurance counterparties?
Insurer financial strength ratings provide evidence about a reinsurer’s ability to meet policyholder obligations. Financial analysis and bank consensus assessments can add context. The review should also consider collateral, contract terms, and the insurer’s dependence on that counterparty.
How can external credit data help surety underwriters?
External assessments can help underwriters challenge their view of a contractor or commercial principal and identify changes during the bond term. They supplement analysis of the principal’s finances and ability to perform its obligations; they do not replace it.
Does weekly credit monitoring satisfy regulatory requirements?
Weekly updates can support ongoing oversight, but frequency alone does not establish compliance. The insurer still needs appropriate analysis and a documented response to material changes, consistent with its applicable requirements.
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