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The Actuary The magazine of the Institute & Faculty of Actuaries
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Banking: Expanding actuarial role

My life as an actuary started in October 2008, only weeks after the collapse of Lehman Brothers and the near failure of AIG. The huge level of uncertainty surrounding the future of the financial services industry made me thankful that I had decided to enter an industry whose members are, as the old saying goes, “accountants that didn’t have the personality for it”. The imminent arrival of Solvency II probably didn’t hurt our cause either and has allowed actuaries to broaden the definition of their roles within insurance companies and pension schemes, with increased involvement at company board level.

However, I believe that the opportunities should not end there. Banking seems like an ideal industry for actuaries to be able to help, given that, much like insurance, its primary aim is to make money by accurately pricing and managing risk. Actuaries are qualified to help with quantitative analysis of risk including building models, setting assumptions and then monitoring and updating them, conveniently the three main blocks of the actuarial control cycle. According to the latest Actuarial Profession annual report, only 2.5% of actuaries are currently employed within retail and investment banks and these are mostly working within pension buyout and investment management teams. In my opinion there is a scope for that number to be increased to at least 15%.

The CDO (Collateralised Debt Obligation) debacle of the last few years has shown us just how damaging financial models can be if not properly understood and monitored by those that use them to inform their decisions. The models exacerbated the crisis due to three main reasons: banks believed that correlations between default rates on mortgages were constant, these correlations were based on only 10 years of data and, probably most importantly, the herd mentality that developed from everyone believing the same things.

The second point is probably the most interesting one. CDOs only really took off once Gaussian copulas were invented. These were useful as they allowed the use of historical data from the Credit Default Swap (CDS) market to determine correlations between mortgage backed securities (MBS) and hence accurately price risk. The problems stemmed from the fact that the CDS market had only really been around for 10 years and so the data that was used for pricing these securities only encompassed a time when international house prices were soaring. When house prices started to decline, the assumptions, and the models in which they were being used, became invalid. We thus moved from a situation where every bank believed that they were making huge profits, having accurately priced their risk, to one where almost all of them were making mammoth losses.

It would be nice to think that this was a one off mistake, but the collapse of Long Term Capital Management for similar reasons not 10 years earlier demonstrates that this isn’t the case. The reasons for the decline of LTCM sound eerily similar: trading was based on strategies that relied on the convergence of bond prices and the correlations between these bonds, and when these correlations turned out to be incorrect - changing because of the Russian government defaulting in 1998, losses started to occur.

These losses were amplified by the enormous amount of leverage that LTCM had taken on because of the small margins that they were playing with. This was a company that employed Nobel Prize winners and even they became blinded by the simplistic assumptions that their work implied, along with the profits they could make.

Both of these cases demonstrate that banks are only too happy to take for granted the results generated by complex models. There is a risk of this continuing as long as models use simplifying assumptions to make them workable. When deriving the parameters to be used in models, developers sometimes argue that shocks and discontinuities to the system must be removed so as to allow the model to fit reality, removing the one component of reality that provides the largest risk to any organisation. Even if you have to remove these events, it is foolish to then imply to management that the prices predicted by the models are always accurate, which is what happened.

In my mind, there is no reason why the role of the quant should not be one that is augmented by the use of actuaries. In theory we are in the ideal position to help them to perform their roles, as the actuarial education gives us an academic background whilst our experience in the financial services sector gives us an understanding of the industry. Whether actuaries would have allowed the use of only ten years’ worth of data in modelling MBSs is something that we will never know, although our experience in other markets has highlighted the limitations of just such an approach.

One of the main aims of Solvency II is to remove the disconnect between those building and running the models and those making the decisions, forcing the board to fully understand and take responsibility for the models and their outputs, which in turn aims to ensure that the builders of the models are able to explain their work to those that are less experienced in the field. I hope the experience of this in insurance would provide useful insights to banks.

I have been lucky enough to work at a firm that doesn’t solely employ actuaries and can bring together teams of people from different disciplines to harness each other’s skills and learn from one another. This has led me to work on projects for public sector organisations, non-financial corporate entities and banks, so broadening my modelling knowledge away from traditional insurance products and into the non-insurance world. None of this would have been possible without the skills that I had been taught by my actuarial team, such as critical thinking when assumption setting and using problem solving to make appropriate simplifications to the models, both skills that all good actuaries have in abundance and ones that would be invaluable in any bank.

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Daniel Zubaida is a consultant for PwC in the General Insurance practice