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Using Credit Analytics to Avoid Mispricing Insurance Premiums

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Using Credit Analytics to Avoid Mispricing Insurance Premiums

Highlights

Mispricing premiums can be very expensive for insurers that guarantee pension payments should a customer not be able to meet its obligations to employees. A detailed analysis of credit risks, including those associated with growing environmental concerns, became a top priority at this insurer to protect its bottom line and future sustainability.

Pain Points

The chief credit officer wanted to enhance his group’s ability to assess credit risks when calculating premiums as policies came up for renewal. In addition, his group was also tasked with developing an internal model to support necessary capital requirements. It was important that any solution included:

  • Robust data and a time-tested methodology for assessing credit risks.
  • Transparency to understand how the model components worked.
  • Access to model documentation as well as validation results to be aware of strengths and any possible limitations.
  • An approach that takes ESG factors into account in the risk analysis.

S&P Global Market Intelligence (“Market Intelligence”) works with many companies in the insurance area, and the chief credit officer had heard about some of the capabilities being used by his peers. He contacted Market Intelligence to learn more about the firm’s offerings. 

The Solution

Market Intelligence first discussed its Credit Analytics solution that blends cutting-edge models with robust data to help users easily evaluate risks with their customer base. The models include: Probability of Default Fundamental Model (PDFN), CreditModel™, and Market Derived Signals Model. Together, they provide the ability to assess companies of any size and put in place an early-warning system to detect possible defaults. Market Intelligence then discussed its Climate Credit Analytics solution for assessing potential environmental risks for companies. These capabilities would enable the credit group to:

Easily assess the creditworthiness of smaller-sized companies

PDFN enables users to evaluate the one- to five-year default risk of public and private banks, corporations, and REITS. PDs can be mapped to quantitatively-derived credit scores (i.e., ‘bbb’) for increased comparability. Workflows are optimized by accessing a pre-scored database leveraging comprehensive and timely data on over 50 million[1] companies globally. Users can determine the default risk of a single company or a portfolio of companies.

Easily assess the creditworthiness of mid- and large-cap companies

CreditModel’s suite of statistical models, trained on credit ratings from S&P Global Ratings,[2] enables users to quickly evaluate the long-term creditworthiness of mid- and large-cap, public and private banks, insurance companies, and corporations globally. The models utilize financial statement and macroeconomic data to generate a quantitative credit score that statistically matches a credit rating from S&P Global Ratings. These scores can be mapped to observed default rates to quantify risk. Analysis can be streamlined by accessing a database of over 58,000 pre-scored entities, going back more than 15 years.

Create an early-warning system

The Market Derived Signals Model is a statistical model that evaluates credit default swap spreads to provide an early warning of potential credit changes and captures the market’s daily view about a company’s perceived risk.

Evaluate ESG factors

Climate Credit Analytics is a climate scenario analysis and credit analytics model suite. The tools combine S&P Global Market Intelligence’s data resources and credit analytics capabilities with Oliver Wyman’s[3] climate scenario and stress-testing expertise. The offering translates climate scenarios into drivers of financial performance tailored to each industry, such as production volumes, fuel costs, and capex spending. These drivers are then used to forecast complete company financial statements under various climate scenarios, including those published by the Network for Greening the Financial System (NGFS), a group of over 80 central banks and supervisors.

Key Benefits

The chief credit officer was impressed with Market Intelligence’s proprietary datasets and capabilities, including financial and industry-specific data, sophisticated quantitative credit scoring methodologies, and ESG expertise. He saw many benefits to the overall offering, including having access to:

  • A transparent and easy to use suite of credit risk models to generate quantitative scores to understand the creditworthiness of customers of all sizes.
  • The ability to score customers individually or in batch mode.
  • A way to detect early signs of credit deterioration.
  • An extensive set of documentation describing the models, plus the results of regular validation tests and model enhancements.
  • Comprehensive and consistent sector-specific modelling of climate risks, including key high carbon-emitting sectors.

Click here to explore Credit Analytics and Climate Credit Analytics mentioned in this case study.  



[1] All coverage numbers as of March 2021.

[2] S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence.  Lowercase nomenclature is used to differentiate S&P Global Market Intelligence PD credit model scores from the credit ratings issued by S&P Global Ratings.

[3] Oliver Wyman is a separate company and is not affiliated with S&P Global or any of its affiliates.

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