How Much Insurance Does an Insurer Need? Data Analytics Transforming Carrier ERM

Big data and analytics are driving a transformation in insurance purchasing decisions, and insurance company chief risk officers are among those being impacted by the wave of change.

Executive Summary

Insurance carriers are not just using data analytics in underwriting and claims, according to Claude Yoder, head of Global Analytics at Marsh, who explains the growing use of analytics by the risk management function. Using the example of an insurance carrier evaluating the adequacy of D&O limits, Yoder explains that analytics are transforming enterprise risk management, giving insurance company risk managers tools to justify insurance purchasing decisions and evaluate capital adequacy.

Carriers are challenging their risk management teams to provide data-driven insight to support recommended insurance purchases and the cost efficiency of the coverage programs they recommend, along with assessments of underlying risk exposures, thereby ensuring that insurance program structures are appropriately aligned to strategic objectives.

As carriers use such information to fully understand the risks of implementing their business strategies, their risk managers are applying enhanced modeling, analytics and insights to their own insurance purchasing decisions. This dramatic industrywide shift toward analytics-based decision-making at every level is changing the way carriers evaluate their enterprise risk and execute their insurance purchases.

Editor’s Note: Side-A policies, like the “A” coverage part of a full D&O A-B-C policy, respond to nonindemnifiable D&O losses—claims for which a corporation can’t indemnify directors because of statutory prohibitions in a state, because the corporation is financially impaired or for some other reason.

Consider this notable example. We’ve recently worked with a carrier seeking greater insight into the adequacy of its directors and officers liability insurance programs. This company’s board requested additional investment in the Side-A-only coverage, and the risk management team was attempting to leverage a traditional peer benchmarking comparison to confirm its program decisions.

Using risk analytics, the carrier’s risk management team was able to run simulations for various class actions and nonindemnifiable scenarios on the company’s D&O program towers, evaluating the response of the BC and Side-A-only coverage. The analysis included the organization’s own loss history, proprietary industry losses and customized “black swan” scenarios, which specifically reflected the company’s risk profile and relatable industry experience.

The result: Data analytics reaffirmed that the carrier’s level of Side-A-only coverage protected the company in nearly every highly unlikely tail-risk scenario and demonstrated that if the company was going to further invest in D&O coverage, it should focus on its BC coverage, as opposed to the Side-A-only coverage, a strategy not previously explored.

This data-driven risk modeling provided the board with what it required for a more informed decision regarding risk transfer—and ultimate approval of the program. Carriers can use this same type of data-driven evaluation for a holistic assessment of underlying risk levels and the efficiency of all lines of coverage, including their professional liability insurance.

Using Data Analytics Across the Enterprise

The insurance industry already relies on highly complex mathematical equations and actuarial models in providing services to its customers. Now, many carriers are using big data and those same tools to transform their own enterprise risk management modeling. By accessing data in real time to review loss trends and cost drivers, including root causes of losses, along with any gaps in loss prevention and claim handling processes, carriers reduce uncertainty in estimating claim values and can more effectively customize their own coverage selections and enterprise risk management solutions.

The advances in data, analytics and technology also are empowering carriers’ business and insurance decision-making in every aspect of the risk life cycle: understanding, offsetting, measuring and ultimately reducing the cost of risk. At each stage, insurers can use predictive modeling and a suite of previously unavailable analytics tools to align risk management decisions with their organizations’ go-forward strategy.

A Forward Look at D&O, Cyber, E&O Risks

Analytics can provide a holistic view of costs to help insurers identify investments that will provide the greatest return and help them make smarter risk management decisions. Deep pools of data previously unavailable in risk modeling can be combined with cutting-edge analytic capability in a simple and mobile-friendly format to understand, evaluate and plan for potential risk exposures in new ways.

Insurers today face a growing set of risk exposures associated with interest-rate and investment risk, government regulatory compliance, mounting cybersecurity threats, and D&O liability. With a sophisticated analytics program, the complexities of D&O liability, in particular, can be managed better. Carriers can structure policy limits properly to ensure sufficient protection and claims response using analytics-backed risk modeling.

Analytics can also help carriers comply with capital requirements under the Solvency II Directive. With risk modeling, insurers can assess capital levels and account for potential volatility, using this as the basis for plans submitted to regulators in an effort to obtain capital release.

Over the next five years, as insurers move in greater numbers to request capital release, analytics will be instrumental in ensuring accurate modeling for capital levels and structuring sufficient risk transfer frameworks. For insurers looking to secure adequate protection from the uncertainty and potential volatility associated with determining capital levels, evolving E&O coverage may be a part of this analysis and financing solution in the future.

Data analytics is leading a transformation in risk modeling and insurance purchasing decisions. At each stage of risk identification, quantification and mitigation, insurance company chief risk officers today have access to predictive modeling and analytics tools previously unavailable in the industry. The result: a more holistic view of risk financing and a more confident alignment of enterprise risk and capital to ensure growth and more effectively manage risk.