About the author – Duncan Zorn is a Senior Actuarial Manager at Deloitte Consulting in Switzerland. He and his team specialise in risk quantification and modelling. Duncan is a Fellow of the Institute and Faculty of Actuaries (DZorn@Deloitte.ch)

We’ve all heard the jokes about actuaries. My personal favourite is this: “How can you tell an extraverted actuary? – he’s the one that looks at your shoes when he’s speaking to you!”, and we all know that the “average” actuary works in an insurance company. However, these stereotypes are fast becoming a thing of the past. The modern actuary is increasingly looking “outside of the box,” and it is becoming common that they are embedded in wider risk management in corporate businesses.

Actuaries love nothing better than to turn a complex problem into a model using their mathematical skills. A risk model is a mathematical representation of a system or process, based on probability distributions of the underlying risk drivers. For example, this may be a model of an uncertain supply chain, operational cash-flow, or any business process. Historical data, as well as the views of risk experts are used to parameterise the model and the risk distributions.

The rise of the “big data revolution” is making relevant risk data increasingly easy to get hold of for the purpose of risk modelling; hence risk modelling is becoming more widespread in many industries. There will, of course, always be some low frequency risks for which data are difficult to find, and in these cases the views of experts are used to compensate for the lack of data.

It is an increasing trend that organisations outside of financial services use actuaries to help them model, quantify and manage risks ranging from operational, political, environmental/weather and compliance through to strategic risks.

The benefits of taking such a quantitative approach to managing these risks include the following; enabling companies to make objective cost-benefit evaluations of risk mitigation options, allowing the assessment of the likelihood of achieving budgets or plans, and improved “risk-adjusted” views of return on investment (ROI). For these reasons, many actuaries are starting to support risk management in expanding fields throughout the public and private sectors.

Increasing applications of actuarial risk modelling
As an actuarial consultant I am fortunate enough to be able to see first-hand how actuarial risk modelling skills are being utilised across corporate businesses. We are increasingly approached by a wide range of clients to support them with actuarial techniques in forecasting and planning, (strategic) investment decision making and risk hedging (including insurance buying) decisions.

Forecasting and planning in many companies are traditionally done by aggregating a series of single point estimates for the drivers of the forecast, with little allowance for the inherent uncertainty of the drivers. Some companies apply a safety margin in their estimates, or conduct single factor sensitivity tests. However, as risks often occur in combination, i.e. are correlated, the single factor sensitivity tests are insufficient, and also give no indication of the likelihood of different outcomes.

Because of this we advise our clients to develop risk-adjusted forecasting models, which show a wide range of forecast outcomes and their associated probabilities. These forecasts incorporate multi-variate risk modelling and stochastically simulate many potential outcomes based on the distributions of the underlying drivers of the particular key performance indicator (KPI).

In order to successfully roll out such an initiative, and to extract the most value, it is crucial that risk management is highly integrated with finance and strategy and the core business.

Figure 1 – Example output from a risk-adjusted forecasting model, showing how the forecast KPI (in this case free cash-flow) is distributed for each future reporting period (RP). On the right the drill down into reporting period 5 shows the probability distribution of free cash-flow and below right the relative contributions of the down-side risks.

The benefits of such a risk-adjusted approach include;

• Improved management understanding of the range of potential outcomes and their associated likelihood;
• Quantifiable evaluation of risk exposure contribution of each risk driver and of associated management actions;
• Quantification of risk drivers supports the creation of risk mitigation strategies. Decisions to avoid, reduce or transfer, hedge or control the most material risks can be based on quantifiable cost-benefit ratios.

In the above, example the risk-adjusted forecast is used to improve cash and liquidity management, but such an approach has many applications. For many of our clients, such as manufacturing or energy companies, forecasting supply and demand uncertainties is crucial to manage stock and avoid shortages with resulting reputational damage or loss of market share.

Investment decision making in today’s increasingly complex geopolitical, economic and regulatory landscape requires a modern sophisticated approach. There are a host of risks and uncertainties that companies face when deciding where to best allocate capital, people and resources. All too often we hear how projects or investments have failed because of some “unexpected scenario”, but in many cases this could have been avoided by a more thorough analytical evaluation at the outset.

When analysing the attractiveness of any investment or project, one of the primary objectives is to grow shareholder value of the company in the so-called risk-return space. On the extremes, we have increasing shareholder value or returns without taking on any additional risk, or reducing risk without any loss in value. In practice any opportunity under consideration is likely to fall somewhere between these extremes.

To ensure optimal decisions companies should evaluate the merits of their capital investments by plotting the expected return/value against the volatility/risk of that value. This was an idea originally developed by Harry Markowitz in relation to financial asset selection in his 1952 publication on modern portfolio theory. Although advancements in behavioural economics in recent years have challenged many of the basic assumptions underlying Markowitz’s paper in relation to its use in financial asset selection (namely rational investors and efficient financial markets), the fundamental concept is still widely used and can be applied to investment decisions of many kinds.

Figure 2 – Growing shareholder value in the risk-return space

Employing such an approach means that companies should consider the risk-adjusted return on any investment and not just the expected return. This approach not only gives a board of directors greater assurance in the decision making process, but also allows their risk preferences (or appetite) to be incorporated into the decision. Both upside and downside potential, and the attaching probability ranges should be calculated. In practise this means using multi-variate risk models to assess investment or project value. As well as this, we strongly recommend incorporating sensitivity and scenario analysis.

• Including sensitivity analysis of primary risk factors enables boards or project managers to quantify the sensitivity of the investment or project value to key sources of risk/uncertainty.
• Performing scenario analysis, where combinations of risk factors are stressed at the same time, gives companies the ability assess the impact of specific potential future events on any capital investment or project . In reality we know that risks often occur in combinations, for example a regulatory or internal approval failure in a project could lead to loss of key staff members and additional development costs.

Both single time point stresses and forward stresses should form part of sensitivity and scenario analyses. Quantification of not only the additional expected return of an investment or project, but also the additional exposure, is now becoming embedded in the risk management approach of many companies. This is something actuaries have been doing for many years in financial services firms. For this reason, and for the reasons mentioned above, actuarial skill sets are being brought into a wide array of public and private sector businesses to support (strategic) investment decision making.

Risk hedging and contingency planning, is one of the more common places to find us actuarial folk. Almost every company will hedge risk of some sort through insurance, and the management of a company’s insurance programme leads naturally to an actuary’s skill set. Often actuaries sit within the captive insurer of the group, should one exist. However, it is increasingly common for actuaries to support hedging and contingency decisions within the core business, and also outside of insurance analysis.

In some industries, to mitigate price risk, hedging for purchases of raw materials and commodities is already widespread. In other industries, the practice is still emerging; however it is increasingly understood that optimising risk-return in purchasing requires an integrated view of earnings at risk under different hedge positions. The techniques discussed in this article are not only relevant to the management of financial or commodity hedging instruments, but can be applied to the cost-benefit evaluation of any hedge or contingency. In simple terms, the quantifiable benefit of a risk mitigation tool or process may be zero in a single ‘best-estimate’ scenario, although the cost of the mitigation is usually not. The benefit of mitigation only kicks-in when adverse events occur, so in order to evaluate the cost-benefit we must model the scenarios and associated probabilities in which these adverse events occur.