Regulation and Risk Culture in the Insurance Industry

Abstract

In this paper, we examine the impact of regulation on the risk culture of U.S. insurance firms, to address the refocused attention on federal insurance regulations after the 2008 financial crisis. To better understand the risk culture of insurers, we apply textual analysis and unsupervised cluster analysis to extract risk culture-sentiment features from 10-K documents and define three distinct risk culture classes. We find that the risk culture of insurance firms is significantly defined by their risk strategies, decisions and recruitment. Comparing the extracted qualitative risk features to quantitative performance measures, we also find that insurance firms with a sound risk culture have higher return on asset, return on equity, Tobin’s Q and tier 1 capital ratios, and more independent and female directors on their boards. A temporal prediction analysis of risk culture over time shows that, large insurers who downgraded into a poor risk culture state have a higher probability of remaining in this downward trend relative to large insurers that improved their risk culture status. Similarly, we observe an improvement in risk culture of large insurers after the Dodd-Frank Act was passed, compared to non-large insurers. Our results suggest that, insurers can benefit from regulations focused on supervising and monitoring their risk behaviors.

Keywords: Risk culture, insurance firms, machine learning, text mining, cluster analysis

JEL Classification: G22, M14, G32, C55.