Credit Risk in Focus: How Insurers Can Strengthen Investment, Underwriting and Reinsurance Decisions with Credit Analytics

On 21 May 2026, S&P Global Market Intelligence hosted a webinar across APAC for Insurers to discuss a credit risk landscape that is becoming more complex, more interconnected and more difficult to monitor using traditional processes alone. The session was led by Katriona Ho, Head of Market Development, Credit Analytics APAC, and Antoine La, Country Director, Credit Solutions. (Missed the live session? Watch the replay here.)

Insurance investment professionals, underwriters and reinsurers may have different mandates, but they increasingly face the same core challenge: how to assess corporate credit risk with greater speed, consistency and confidence.

Investment teams need to evaluate companies before allocating capital to bonds, private credit or other investment opportunities. Corporate underwriters need to assess companies before assuming insurance risk through products such as trade credit, surety or Directors & Officers (D&O) insurance. Reinsurers face similar demands as they assess ceded corporate risks and manage accumulated exposures across portfolios.

Despite these different use cases, the fundamental questions are often the same:

• How resilient is this company today?
• How could its credit profile change under different market conditions?
• Which early warning signals could indicate deterioration before losses materialize?
• How can teams make decisions faster without compromising analytical discipline?

As insurers increase exposure to private credit, complex corporate risks and more volatile market conditions, traditional approaches based primarily on historical financial statements are becoming increasingly stretched. The opportunity now is to move from reactive credit review to forward-looking credit intelligence.

That is where Credit Analytics, from S&P Global Market Intelligence, can bring meaningful value: by helping insurers connect fragmented information, assess risk consistently, monitor changes earlier and communicate decisions more clearly.

Better credit decisions require better workflows, not simply more data

Insurers are not short of information. They are often challenged by how much time it takes to collect, validate, reconcile and interpret that information before it can support a decision.

During this webinar, 50% of participants identified data ingestion and document processing as the largest bottleneck in their current credit assessment process. While one-third cited a lack of timely insights.

This highlights a common issue across the insurance value chain. Investment teams may spend significant time gathering financial statements, validating company information and preparing credit views before assessing private credit opportunities or conducting portfolio reviews. Underwriters may need to reconcile financial information, company disclosures, market news and external intelligence before making decisions on larger or more bespoke corporate risks. Reinsurers must evaluate not only individual ceded risks, but also how those exposures accumulate across sectors, geographies and counterparties.

The result is that highly skilled professionals can spend too much time preparing the analysis and not enough time applying judgement to the decision.

Credit Analytics helps address this challenge by streamlining the credit assessment workflow. Instead of relying on manual processes and disconnected data sources, insurers can bring together company financials, market signals, peer comparisons, probability of default measures, scenario analysis and credit reports within a more consistent analytical framework.

The benefit is not simply faster processing. It can support more efficient use of expert time. By reducing manual effort and surfacing relevant risk drivers earlier, Credit Analytics enables investment, underwriting and risk teams to focus on the decisions that matter most.

Credit assessment is evolving beyond financial statements

Financial statements remain a critical part of corporate credit assessment, but they are no longer sufficient on their own.

A company’s financial performance can explain where it has been. Insurers also need to understand where it could be going. That requires a broader view of company resilience, including ownership structure, industry dynamics, macroeconomic conditions, market events, peer performance and early warning indicators.

The webinar audience reflected this shift toward more holistic credit analysis:

• 60% already combine multiple sources of company information during credit assessment.
• 80% are either piloting or actively incorporating unstructured information—such as company filings, documents and news—into their workflows.

This shift matters because credit deterioration rarely appears in one place at one time. It can emerge gradually through weakening fundamentals, negative news flow, deteriorating sector conditions, rising leverage, liquidity pressure or changes in market sentiment.

Credit Analytics helps insurers bring these signals together in a more structured way. By combining financial data, company intelligence and forward-looking credit measures, teams can develop a richer view of risk across both investment and underwriting exposures.

For investment professionals, this can support more informed screening, due diligence and portfolio surveillance. For underwriters, it can help strengthen risk selection, pricing discipline and limit management. For reinsurers, it can improve visibility into ceded risks and accumulated exposures.

This approach can help support a more complete understanding of corporate credit risk — not only based on what a company has reported, but also on how its risk profile may evolve.

AI should accelerate judgement - not replace it

Artificial intelligence (AI) is rapidly becoming part of insurers' credit assessment processes, particularly in research, document processing, monitoring and report generation. Our webinar survey respondents indicated they expected AI usage to increase over the next three years, with:

• 71% expecting gradual adoption
• 29% expecting significant expansion

However, adoption remains measured rather than fully mature.

• 43% have introduced automation for selected workflows.
• 29% are still in experimental stages.
• Only 14% have fully integrated AI into credit decision-making.

For insurers, the greatest opportunity is not to replace human judgement, but to improve how quickly and effectively that judgement can be applied.

AI-enabled capabilities can help automate repetitive tasks such as extracting information from documents, summarizing company developments, drafting credit memos and flagging relevant changes. This can reduce the time analysts and underwriters spend on manual preparation, while improving consistency across teams.

But in insurance, explainability is essential. Credit decisions often need to be reviewed by investment committees, underwriting managers, risk teams, regulators and senior leadership. Outputs must be transparent, auditable and supported by clear evidence.

That is why AI-assisted credit assessment works best when it is combined with robust analytics, transparent methodologies and human oversight. The goal is not automated decision-making in isolation. It is decision support that can help professionals work more efficiently, communicate more clearly and maintain confidence in the analytical process.

Supporting  more consistent and informed credit decisions across the insurance value chain

Credit Analytics can help insurers create a more consistent foundation for credit risk assessment across investments, underwriting and reinsurance.

Rather than treating each workflow as a separate process, insurers can use a common analytical framework to support the full credit lifecycle — from initial screening and due diligence to portfolio monitoring, scenario analysis and risk communication.