Author Archives: Andrew Coburn

About Andrew Coburn

Senior Vice President, LifeRisks, RMS
Andrew oversees model development for RMS LifeRisks. He is responsible for maintaining a world-class research team specializing in a wide variety of disciplines, including demographics, economics, advanced financial modeling techniques, natural sciences, and behavioral sciences. Since joining RMS in 1998, Andrew has led model development for new classes of risk and markets, producing the first probabilistic terrorism risk model and a probabilistic longevity risk model, and conducting pandemic and infectious disease analysis. His current research includes the investigation of modeling solutions for understanding financial catastrophe risk, and non-linear effects of extreme catastrophes on society and the economy. With more than 27 years of experience in the field of risk management, Andrew is a recognized authority in insurance risk analysis, and has authored six books. He is the director of the Center for Risk Studies, and a member of the ICRM and Financial Network Analytics. He holds a PhD in risk management policy from the University of Cambridge.

Using Network Theory to Understand the Interconnectivity of Financial Risk

For today’s regulators, systemic risk remains a major issue. Tracing the connections between financial institutions and understanding how different mechanisms of financial contagion might flow through the system is complex.

Modern finance is a collective of the activities of tens of thousands of individual enterprises, all interacting in a “living” system. Today, nobody truly understands this system. It is organic and market-driven, but the fundamental processes that drive it occasionally collapse in a financial crisis that affects us all.

The increasing risk of financial contagion in the financial industry has triggered a new discipline of research – called “network theory in financial risk management” – which is quickly gathering pace. These valuable studies aim to identify and analyze all possible connections between financial institutions, as well as how their interconnectivity can contribute to crisis propagation.

Later this month, will launch the Journal of Network Theory in Finance. This journal will compile the key papers of financial risk studies worldwide to provide industry participants with a balanced view of how network theory in finance can be applied to business.

Papers from the inaugural edition of the new journal will be showcased on September 23 at the Financial Risk & Network Theory conference, which is hosted by the Centre for Risk Studies at the University of Cambridge. I will be presenting a keynote on how catastrophe modeling methodologies can be applied to model financial risk contagion.

Our financial institutions are connected in a multitude of ways. For example, by holding similar portfolios of investments, using common settlement mechanisms, owning shares in each other’s companies, and through inter-bank lending.

As the interconnectivity of the world’s financial institutions and markets deepens, financial risk managers and macro-economic planners need to know the likelihood and severity of potential future downturns, particularly the “tail” events of economic catastrophe. Companies must continually understand how they are exposed to the risk of contagion; many were surprised by how fast contagion spread through the financial system during the 2008 credit crunch.

The regulator’s role in limiting the risk of future financial crises includes identifying Systemically Important Financial Institutions (SIFIs) and understanding what aspects of a SIFI’s business to monitor. Regulators have already pioneered network modelling to identify the core banks and to rank their systemic importance, and can now demand much higher standards of risk management from the SIFIs. Increasingly, similar models are being used by risk practitioners and investment managers.

The studies of network theory in financial risk management, such as those carried out by the Centre of Risk Studies, provide valuable insight for all risk practitioners involved in managing financial risk by providing a robust foundation of science from which to understand, model and, ultimately, manage financial risk effectively.

Social Change is Outperforming Medical Science

We are in the middle of a health awareness revolution.

Attitudes to fitness, health, diet, and social risk factors are changing more rapidly than at any time in history. This has fueled a massive increase in life expectancy, particularly in better-educated social groups. Actions by individuals taking responsibility for their own health have outstripped the benefits of modern medicine in driving recent mortality reduction.

It also appears that the appetite for health-risk information is outstripping the capability of medical science to provide it. This is problematic not only for the medical profession, but also for the financial services industry in funding our retirement provisions.

The recent furor in the American Heart Association and American College of Cardiology is about the accuracy of risk models in new guidelines published last week. Risk models are used to help individuals make decisions about actions to improve their health. In this case, models were used to produce guidelines for taking statin drugs to reduce blood cholesterol – a leading risk factor for heart disease.

Medical-risk models take volumes of historical statistical data and deconstruct the importance of a large number of variables to try to assess their relative importance. The human body is a very complex system – it is not a piece of engineering that can be easily subjected to analysis using the laws of physics. It has many interacting biological processes and interdependencies, and human bodies have wide variations in characteristics in any population.

Because risk models need large volumes of data to tease out all the different variables that apply to an individual person, the historical data needs to be collected over a long time period. The newly-released calculator is based on data from the 1990s when many of the social habits and medical practices were very different than present – for example the gap between male and female mortality has narrowed significantly in the past 20 years. Leading cardiologists argue that these new guidelines have failed to keep up with and anticipate all the recent changes in patterns of public health and life expectancy.

Past health patterns aren’t a great guide to the future.

Similar problems also underpin the life expectancy estimations made by annuity providers and life insurers, who use past mortality data to project life expectancy in future decades for their retirees and pensioners. The fact that most pension liabilities are under-funded is not new news, yet solutions to ensure the future financial health of our elderly population are only as effective as the reliability of the underlying life expectancy projections.

Projections that fail to properly consider how the future may differ from the past, whether due to lifestyle or biomedical advances, can lead to the wrong strategies. Medical risk models developed by organizations like the American Medical Association suggest that even without future biotech advances, mortality rates could almost halve again from present rates if more people adopted highly healthy lifestyles. Models, such as those developed by Risk Management Solutions, incorporate all the variation that future mortality trends might follow and suggest that there is a 1-in-100 likelihood of a future mortality trend that would cause a trillion-dollar increase in annuity liabilities for the global pensions industry.

Improving medical risk models to ensure that they incorporate the potential for changes in the patterns of public health and life expectancy is a high priority for modern society, feeding into future healthcare planning, the provision of accurate advice for individual decision making and for the future financial health of our elderly population.