Assessing Risk: How Big Data and Analytics Provide Insight

The days where crises were rare events seem to be a distant memory, with a large number hitting the headlines in recent years. When a crisis is not managed effectively, the impact can be severe – either financially or in terms of damage to corporate reputation. It is therefore essential that the treasurer, as manager of an organisation’s financial assets and liabilities, takes the appropriate steps to understand the crises they could potentially face and introduce frameworks that allow them to prepare for, manage, and respond to such events.

In our experience, organisations that adapt to emerging trends, successfully anticipate risk and manage issues before they turn into crises, are best positioned to achieve optimal performance and improve competitiveness over the long term.

When a crisis does occur, an organisation needs to act decisively and demonstrate a state of control, in order to manage the event and control its impact. For this reason, management needs to be prepared to make quick decisions and act decisively, even when there is limited information available. For crisis management to be effective, all plans, procedures, and response structures need to be prepared in advance, with training and testing taking place periodically to ensure the response framework will be fit for purpose.

What tools are available to support the treasurer’s decision-making process in preparing for the financial impact of a crisis, to have appropriate risk transfer mechanisms in place, and manage the risk transfer costs?

The Role of Big Data and Analytics

Every company is confronted with the challenge of allocating capital to generate growth and protect against risks. Managing a company’s long-term financial performance requires that risk is assessed and addressed in an effective way. Historically, it has often been difficult to predict the degree of financial impact that potential risks could have on a company. However, big data and analytical tools can not only help to quantify and assess risks, it can drive decisions around the financing of these risks – either by using the company’s own balance sheet or other hedging options, such as insurance. Assessing the frequency and severity of exposure to both insurable and non-insurable risks in this way enables companies to frame risk-financing decisions in the context of their individual corporate risk profile.

By tapping into a seemingly endless pile of data, companies can form new insights around their own risk exposures, and also benchmark themselves against peers across their industry. Big data and analytics can be used to establish an understanding of the potential costs and overall financial impact of unknown risks. This may include using a global database of aggregate loss data from countries and companies around the world to gain a more credible view into the expected losses and associated volatility of risk exposures. The resulting output can then frame discussions around risk mitigation strategies, crisis management and risk transfer mechanisms.

Big data and analytics are changing – if not revolutionising – risk management, and giving treasurers valuable tools to deal with the potential capital impact of crisis situations and contingency planning. Being able to efficiently allocate capital to the risk exposures faced by an organisation requires an understanding of the cost of each source of capital that is accessible in a crisis situation: equity, debt or insurance. This holistic view of risk and the associated costs of risk are fundamental to determine the optimal risk transfer solution.

Economic Cost of Risk

Marsh has developed the concept of Economic Cost of Risk (ECoR), which enables companies to calculate the cost of keeping a risk on balance sheet, versus the cost of using other risk transfer mechanisms, such as insurance. A key component of ECoR is an ‘implied risk charge’, which recognises that there is a real – although not obvious – financial cost to an organisation created by the possibility of unexpected losses (for example, a catastrophic loss that exceeds the amount of insurance coverage purchased). Such unexpected losses absorb capital; the implied risk charge represents the cost of capital exposed to these unexpected losses.

Most companies do not consider implied risk charges in their quantification of the cost of risk when they make risk financing decisions; however, only by quantifying the ECoR for various risk financing alternatives can a company choose the optimal option. This analysis can be done both on an individual risk basis and across the risk portfolio for both insured and uninsured risks. The portfolio view allows companies to optimise their risk financing decision made in light of their overall financial goals, risk tolerance and risk appetite.

For example, once the potential volatility is understood, calculating ECoR for alternative hedging options enables an assessment of the optimal structure, based on expected losses, premium charge and prevailing weighted average cost of capital (WACC) – as shown in Figure 1 below.

Figure 1: Devising an Optimal Structure for Risk Hedging Options:

Marsh structure for risk hedging options

Source: Marsh

ECoR drives a disciplined approach to risk management and risk financing decision making. It supplements the treasurer’s toolbox of financial metrics and key performance indicators, and helps to inform strategic business decisions around risk and capital. The analysis also informs the capital benefits of using (or not using) formal risk retention vehicles such as a protected cell or a captive insurance company.

Big data helps organisations to link cause and effect, using company and industry-specific data to allow for benchmarking and “what good looks like”. Modelling techniques such as Monte Carlo simulation can then help extract value from the data and generate insights. This can be a very powerful tool for organisations as a whole, and for treasurers in particular, who need to understand the financial perspective of such events.

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