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Directors and Officers Liability

The focal point of our Directors and Officers Liability assessment is the threat of shareholder litigation, which under most circumstances is the most economically and reputationally damaging aspect of Executive Risk. Starting with securities class action (SCA) filings, we continuously track the development of individual legal matters conducting thorough research to identify dispositions of individual cases, which includes tracking down costs of settled cases in our SCA Tracker database. We recently completed a comprehensive, 25-year look at the post-PSLRA patterns of frequency and severity of SCA - a copy of the resultant research study can be found here.

Focusing on all public companies traded in North America (organized stock exchanges + OTC), our SCA Tracker database combines post-PSLRA ('96 onward) SCA filings & settlements with key financial events (IPO, M&A, restatements, etc.), financial performance details and stock price volatility. Making use of multivariate statistical methods, we estimate company-specific SCA incidence and cost exposures to inform D&O insurance and risk mitigation decision-making. Our estimation methods explicitly differentiate between financial and industrial organizations, in addition to also adjusting for industry and market capitalization differences, all while addressing key legislative (PSLRA, SOX) and judicial (Dura Pharmaceuticals, Cyan and other) provisions and developments, paying particular attention to loss causation/scienter related matters.

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Our impact estimation is manifestly non-linear: When looked at from the perspective of company size, the resultant estimates, viewed as % of value, tend to somewhat increase for smaller companies, and decrease for large firms, which reduces the chances of arriving at unrealistically small or large exposure values. In addition, our company-specific impact estimates are expressed as pseudo credible intervals, or low and high ranges, to capture the observed variability in SCA losses among otherwise similar cases.

In spite of the relative complexity of our SCA Likelihood and SCA Severity estimation processes, our SCA exposure reports are easy to interpret, while at the same time offering a wealth of company-specific and peer benchmarking details, which jointly yield a comprehensive assessment of shareholder litigation exposure (click here for a sample). All estimates are rooted in the context of industry sector - market capitalization tranche segments; moreover, the severity estimates are expressed in terms of 'expected', or most likely, and 'catastrophic', or maximum probable, loss scenarios, with the former framed in the context of frequentist confidence intervals and the latter framed in the context of Bayesian credible intervals.

In addition to our company-specific risk exposure models, we also offer general benchmarking and ad hoc risk analytical services:

Securities Litigation Benchmarking

While broad industry sectors such as life sciences or financial services hide a considerable degree of within-sector / cross-company variability, there is nonetheless value in considering sector benchmarks as a general reference point in evaluating threats posed by adverse actions of organizational shareholders. We use the rich variety and depth of SCA filings, settlements and firmographic data outlined above to provide clients with aggregate industry sector benchmarking summaries illustrated opposite:


Explanatory Analytics:

Root Causes of Observed Outcomes

There are situations where an organization might want to either develop its own set of outcome-predicting models or conduct more in-depth causal analyses but might not have the requisite in-house data modeling capabilities. In those situations, organizations generally have two competing choices: 1. to build up their internal staff, or 2. to use outside consultants. Obviously, there are pros and cons to both: Building an in-house team of adequately qualified data scientists might be an appropriate course of action for a large organization where demand for advanced statistical analytics skills remains strong over time; on the other hand, it might be cost-prohibitive for smaller organizations, or those where advanced data analytical expertise is needed on periodic, rather than constant basis.

For example, an organization might want to develop predictive models focused on specific outcomes, such as product liability claims. Engaging outside, highly experienced data science consultants may be the preferred option here as it gives the organization access to the "best of breed" practices, while also limiting its financial commitments. Under such a scenario, the organization would incur the initial cost of model(s) development, following which only a fairly nominal costs of ongoing model upkeep and maintenance.

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Exploratory Analytics:

Mining Text & Numeric Data

"Data-rich and information-poor" is a common complaint voiced by organizations which, on the one hand, diligently capture and store lots of transactional and related data, while on the other hand struggle with turning the ever-expanding volumes of data into competitively-advantageous insights.  In keeping with the "learn to walk before attempting to run" mindset, our exploratory data mining services offer open-ended, exploratory mechanisms for identification of meaningful patterns in the available data; also, can serve as a point of departure in the metadata development process.

Noting that the vast majority of all data available to organizations is coded as text, our data mining capabilities encompass both numeric as well as text data.  Furthermore, to the degree to which the two data types can complement each other, when it is appropriate and possible, we take steps to amalgamate textual and numeric data to enhance the richness of analysis-derived insights.  Lastly, we expressly differentiate between statistically and practically significant findings, with the ultimate goal  of highlighting those associations that exhibit the greatest chances of materially improving the efficacy of key organizational decisions.

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