The existing empirical research on insurer insolvency relies almost exclusively upon individual insurance company financial data, even though the insurance industry is dominated by group-affiliated firms. This is the first study to evaluate the benefit of using group-level data to predict insurer insolvencies for group-affiliated insurers. The study uses financial ratios from the NAIC FAST scoring system, measured at both the company level and group level, as potential predictor variables. The results indicate that group-level financial information substantially improves the predictive power of an insolvency prediction model relative to a model that uses only the analogous company-level variables. In fact, the group-level variables are found to often be substantially more powerful than company-level variables in predicting individual insurer insolvencies. These results suggest that future insolvency analysis should, whenever feasible, include group-level information to obtain higher predictive accuracy.