Financial and insurance industries have long understood the value of quantitative analysis of operational information in estimating loan and claim risk in business practices (e.g. granting loans, setting interest rates and premiums). Firms within the financial and insurance industries have historically invested in processes and technologies to identify and estimate risk exposure. These processes use data analysis to assist with monitoring risk exposures and maximize risk-based business decisions. Despite these investments, the banking industry, expected to be a leader in risk assessment and management practices, has had several firms experience major failures managing organizational risk. More specifically, the reputation for bank risk-taking strategies has been criticized as a leading contributor to the recent Great Recession (December 2007 – June 20091 ). There also have been other notable examples of operational practices having significant impacts on banks in recent years. For example, Barings Bank (1995) and J.P. Morgan Chase (2012) each allowed a single employee excessive authority to make extremely risky equity trades. Although J.P. Morgan Chase was able to absorb a $5.8 Billion loss2 (original estimates of losses were as high as $9 Billion3 ), Barings Bank was not able to survive the risky trades made by Nick Leeson and was sold for £۱٫ While not having a global economic impact, several other major firms have experienced significant losses as a result of gaps and failures within their risk management strategy and the security of customer information.4 In order to address the lack of a systematic enterprise-wide risk management plan, in 2004, the Committee of Sponsoring Organizations (COSO) of the Treadway Commission created an Enterprise Risk Management framework (COSO-ERM). COSO-ERM defines Enterprise Risk Management (ERM) as an enterprise-wide risk assessment and management process designed to “provide reasonable assurance regarding the achievement of entity objectives.” Although adoption of risk management may not specifically change the level of organizational risk, it likely impacts the actual measurement and monitoring of risk throughout the firm. As a result of targeting specific levels of risk, firms are likely to reduce downside operating performance volatility while accomplishing their ordinary business goals and objectives which include generating profits and providing shareholder value. Moreover, COSO’s definition of ERM implies that firms implementing ERM processes should be more likely to achieve enhanced operating and market performance, yet this empirical link remains unclear. Recently, Monda and Giorgino (2013) note that empirical studies have provided little evidence on the effect of ERM on firm value. In addition to the noted empirical limitations, they state that “despite the theoretical motivations, if and to what extent ERM adds value is yet to be proven.” While McShane, Nair, and Rustambekov (2011), Baxter, Bedard, Hoitash, and Yezegel (2013) rely on the financial services industry to examine ERM benefits, Monda and Giorgino (2013, p. 3) further indicate the limitation of such studies to financial institutions which differ substantially from industrial firms in institutional type and operations.