Blog — August 12, 2025

How the Right CECL Partner Improved Earnings Confidence and Freed Up Staff Time

Client Profile:
A U.S. GAAP insurance company managing a portfolio of fixed-income assets

Primary Users:
Investment accounting, finance

Navigating the complexities of CECL (Current Expected Credit Loss) standards is a significant challenge for companies managing financial assets. These standards mandate the estimation of expected credit losses over the life of assets, influencing earnings, audit readiness, and operational efficiency. Many companies initially rely on legacy CECL providers, which often prove inadequate due to incomplete coverage, opaque methodologies, and cumbersome data processes. These shortcomings result in understated earnings, as conservative estimates are necessary when data is incomplete or unclear, and increase the internal workload as staff must manage data and justify estimates to auditors.

The transition to modern CECL solutions can markedly improve operational efficiency and earnings confidence. These solutions provide comprehensive coverage across diverse asset classes, transparent methodologies, and audit-ready outputs, which streamline audit processes and reduce the need for extensive internal justification. The integration of advanced technology platforms enhances the completeness and convenience of these solutions, offering configurability for client-specific rules and scenarios, and allowing for a tailored approach to credit loss estimation.

This case study illustrates the success of a U.S. GAAP insurance company that switched to a CECL solution from RiskSpan and S&P Global Market Intelligence. This transition yielded stronger earnings confidence and freed up staff time by providing robust coverage and transparent outputs, enabling the company to focus on strategic initiatives rather than compensating for the inefficiencies of their previous provider.

Read the full case study to learn more and discover insights and strategies that could redefine your approach to credit loss estimation. 

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