How One Insurer Made CECL a Strength, Not a Struggle
Read the full S&P Global Market Intelligence case study: https://riskspan.com/spgmi-case-study-cecl-coverage/
Meeting the CECL standard shouldn’t mean draining internal resources or compromising earnings confidence. Yet for one insurance company, a legacy CECL provider did just that—leaving major gaps in asset class coverage, opaque methodologies, and audit headaches in its wake.
This case study highlights how switching to RiskSpan and S&P Global Market Intelligence turned things around. Together, we delivered a fully managed CECL solution that provides complete portfolio coverage, robust auditability, and significant efficiency gains.
Powered by S&P’s macroeconomic scenarios and backed by RiskSpan’s platform, the solution gave the insurer stronger confidence in its estimates, streamlined audit reviews, and freed up key staff to focus on higher-value work.
For investors and finance teams evaluating CECL providers, this success story underscores the value of working with a partner that combines top-tier data, flexible modeling, and hands-on support.
Read the full case study and see how RiskSpan and S&P Global Market Intelligence make CECL compliance a strategic advantage.

Let’s first consider the sample required for a single pool of nearly identical loans. In the case of a uniform pool of loans — with the same FICO, loan-to-value (LTV) ratio, loan age, etc. — there is a straightforward formula to calculate the sample size we need to estimate the pool’s default rate, shown in Exhibit 1.1 As the formula shows, the sample size depends on several variables, some of which must be estimated:
In this case, our internal sample of 500 loans is more than enough to give us a statistical confidence interval that is narrower than our materiality thresholds. We do not need proxy data to inform our CECL model in this case.
Again assume we segregate loans into four categories based on this third variable. Now we have 4^3= 64 equal-sized buckets. With loan-level modeling we need around 12,000 loans. With bucketing we need around 100,000 loans, an average of around 1,600 per bucket. As the graph shows in Exhibit 2, a bucketing approach forces us to choose between less insight and an astronomical sample size requirement. As we increase the number of variables used to forecast credit losses, the sample needed for loan-level modeling increases slightly, but the sample needed for bucketing explodes. This points to loan-level modeling as the best solution because well-performing CECL models incorporate many variables. (Another benefit of loan-level credit models, one that is of

