Data analytics can help risk managers to demonstrate to senior management a more analytical and strategic approach to risk management.
The world has developed an obsession with data and the insurance industry is no exception. But it is not always sure what to do when given more data.
Ultimately the importance of ‘big data’ – as it is often fashionably labelled – lies not in its quantity but in its quality and, most importantly, in how it is used: commonly referred to as data analytics.
Data analytics enables companies, working in conjunction with brokers and insurers, to profile their risks and risk appetite in far more granular detail and, using that information, to make better-informed decisions about their risk management and risk financing arrangements.
“Data analytics enables companies to move beyond just the pricing and placing of insurance and reinsurance and to create a bespoke financing strategy that is driven by an understanding of the interaction of enterprise risk management and the need to deliver shareholder value to a business,” says David Flandro, Global Head of Analytics at JLT Re.
“If a risk manager can start showing how a better understanding of risk and the trade-off between retaining risk and transferring risk affects share prices and returns to investors then boardrooms start to take notice,” adds Flandro.
A fundamental shift in the role of the broker lies at the heart of a greater data analytics, says Hamish Roberts, Business Development Director of JLT Specialty.
“We can take client and industry data and expert opinion from key internal stakeholders to create bespoke risk financing programmes to take to the market, but there is the opportunity to do much more than that.
“Brokers can help their clients have an internal discussion around the analysis of their data so they can buy insurance in a more strategic and optimum way, and make improvements to their broader risk management strategies,” says Roberts.
Opening up these broader discussions helps to make risk and insurance a strategic issue that receives more board-level focus.
With years of soft markets in most classes, for some companies insurance is less of a board subject than ever, as it has become commoditised and relegated to a cost centre that can be managed at an operational level.
In many cases insurance strategies, sometimes even the programmes themselves, have not changed in years and can be improved to better match companies’ risk profiles.
Bringing powerful analytical tools to bear on a firm’s data can reverse this trend, however, says Michelle Mason, Head of Client Management, Property & Casualty, at JLT Specialty.
“By using analytics to enhance understanding and quantify risk, companies and their boards are able to make more informed decisions about risk retention, and how to finance risks, considering the merits of vehicles such as captives.
Total costs of risk
Companies will also be able to potentially look at risk financing solutions for risks previously held on the balance sheet, says Mason. “They will be able to look more completely at the total cost of risk – retained losses, risk transfer costs and the cost of capital.”
Some sectors are already ahead of this curve – including some large life science companies – and the strategic impact is clear, says James Bird, Head of the Life Science Risk Practice at JLT Specialty.
“Using this greater insight, a company might decide to buy less insurance in some areas because there is little volatility and the worst possible loss is tolerable to the company.
“Conversely, there might be good strategic or tactical reasons why a company might buy insurance in areas previously uninsured or underinsured,” says Bird.
“Ultimately data analytics enables a company to more keenly understand these reasons and develop a strategy based on data linked to the impact on their own financial metrics, rather than employing too much guesswork.”
Insurers and insureds traditionally have good data on losses for property and natural catastrophe and other risks but other areas can be blind spots.
“Product recalls, regulatory shutdowns, failure of suppliers and cyber events are examples of risks whose impacts are not often adequately identified or quantified. It is sometimes difficult to find that data within your own company,” says Bird.
“Identifying data elsewhere, however, such as in publicly available resources, and supplementing it with client data can shed new light on previously hidden risks and their potential costs,” Bird adds.
For this approach to really be successful, insured companies and their insurance brokers need to accept that the insurance-buying process has evolved, as different skillsets are brought in, says Roberts.
“Our approach to market is a process of discover-develop-discuss-deliver. This expertise has always existed in the reinsurance arena because insurers reinsuring into the market through brokers describe their portfolios by analysing them to the nth degree.
“We are now seeing this expertise brought into the mainstream and used among direct insureds,” says Roberts.
Data analytics four-stage process before buying insurance
- Discover (agree strategy)
- Develop (gather data and analyse risk)
- Discuss (discuss findings with client)
- Deliver (go to insurance market to collect terms)
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For more information please contact Hamish Roberts, Business Development Director on +44 20 7528 4141