Posts Tagged ‘Models’



November 28th, 2016

MMC Cyber Risk Handbook 2016: Increasing Resilience in the Digital Economy

Posted at 1:00 AM ET

The Cyber Risk Handbook 2016 presents views from Marsh & McLennan’s cyber leaders and leading third-party experts with whom we collaborate on how companies can assess cyber risks, develop comprehensive strategies and align their people to bolster cyber risk management.

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November 23rd, 2016

Measuring Cyber Aggregation Risk

Posted at 1:00 AM ET

Julia Chu, Managing Director, Guy Carpenter and Ashwin Kashyap, Director of Product Management, Symantec Corporation

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Cyber risk is now an embedded feature of the global risk landscape, and preventative risk management and post-event remediation are gaining importance as shareholders, customers, supply chain partners, and regulators are increasingly focused on how companies are managing for cyber risks. Insurance is becoming an important piece of the strategy for helping businesses address these risks.

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November 10th, 2016

Newest Versions of Patented Capital Modeling Tools Enhance Automation and Integration, Estimate Inflationary Risk, and Improve Run-Time: Guy Carpenter Introduces MetaRisk® Reserve™ 5 and MetaRisk® 9

Posted at 12:30 AM ET

Guy Carpenter today announced the launch of MetaRisk® ReserveTM 5 and MetaRisk® 9, the latest updates to its powerful suite of capital modeling tools built on more than 25 years of research and development.

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October 31st, 2016

Reserving and Capital Setting: Sizing the Problem, Part III: Quantifying Emerging Risks; Expert Judgement

Posted at 1:00 AM ET

Data quality and availability should also be examined in depth. Because the risks are new, the data may not be captured correctly to power the model, which will lead to further uncertainty and may even preclude the use of a model altogether.

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October 27th, 2016

Analytics: Fueling Risk-Informed Decisions

Posted at 1:00 AM ET

tim_059_headshot Tim Gardner, CEO of U.S. Operations

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Today’s rapidly changing global environment presents insurers with many challenges and opportunities as capital management and risk transfer techniques evolve at an unprecedented pace. Stakeholders, regulators and ratings agencies are deepening their focus on risk management practices, and revolutionary developments in technology, including the Internet of Things and hyper-connectivity, are driving companies to adapt to the challenges that senior management faces to support risk management decisions material to their business.

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October 23rd, 2016

A.M. Best’s More Transparent Ratings Criteria Provide Benefits to Insurers That Proactively “Own Their Ratings”

Posted at 1:00 AM ET

esimpson-hs-smEric Simpson, Managing Director

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Maintaining or improving ratings is a priority for most insurers. This can be challenging amid increasing demands for companies to “own their risk” (Own Risk and Solvency Assessment “ORSA”) in an environment of evolving rating agency requirements, including A.M. Best’s (Best) proposed ratings methodology and Stochastic-based Best’s Capital Adequacy Ratio (BCAR) criteria.

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October 20th, 2016

Reserving and Capital Setting: Sizing the Problem, Part II: Quantifying Emerging Risks; Models

Posted at 1:00 AM ET

Once the risks have been identified and ranked, the next step is how to quantify the likely impact on the financial results of the firm. The first and most obvious question is what available quantification techniques are available for each risk on the list. This will depend on the availability of relevant data and commercially produced models.

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October 17th, 2016

Tracking and Modeling New Integrated, Intricate Technology Risks

Posted at 1:00 AM ET

Casualty (re)insurers do not cover standalone emerging risks. A product defect (with recall) or a latent bodily injury resulting from new technological nano-products or Unmanned Aerial Systems risks, could lead to class action lawsuits and ultimately large liability claims including products liability as well as professional liability. This emergent reality, however, is difficult to address. A carrier would need to identify and model several possible epicenters of a liability chain reaction and follow their rapidly spreading implications throughout a portfolio. Without new powerful casualty modeling capabilities as well as highly granular data on the products and subcomponents that each of their insureds manufacture and sell globally, this process would be time-consuming, impossible to complete and likely to miss key threats and underlying exposures.

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September 27th, 2016

Longevity Risk

Posted at 1:00 AM ET

The impacts to society from changes in longevity and life expectancy will be wide-ranging and incredibly difficult issues to grapple with. A 2012 International Monetary Fund (IMF) study revealed that if individuals lived three years longer than expected the cost of aging could increase by 50 percent. This translates to 50 percent of 2010 gross domestic product (GDP) in advanced economies and 25 percent of 2010 GDP in emerging economies. Globally that amounts to tens of trillions of US dollars. The United Nations expects the aggregate expenses of the elderly will double over the period between 2010 and 2050. The figure below shows the projected trend of rising life expectancy to continue in all regions of the globe regardless of economic advancement.

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September 26th, 2016

Increasing Confidence and Transparency in Your Catastrophe Risk Decisions: Part II

Posted at 1:48 AM ET

thomas_sherry_sm1james-burnett-herkes-sm1Sherry Thomas, Head of Catastrophe Management - Americas and James Burnett-Herkes, Senior Vice President

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Model Suitability Analysis (MSA)® consists of a set of standard tests and protocols that benchmark the models against independent reference data for hazard, event frequencies, damage functions, losses and historical experience. These datasets are created by independent and credible third-party research institutions that have expertise in the respective subjects. Rather than reinventing the wheel and developing models that already exist, the MSA approach evaluates the scientific underpinnings of existing models to establish confidence where warranted, and to identify areas of uncertainty. Guy Carpenter aggregates this information into our MSA Knowledge Base, and establishes standard protocols that are efficient to execute and test all models using the same standard procedure to achieve homogeneity and fairness in the process.

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