Need to up your risk management game? Analytics provide an answer
Data, analytics, and technology can help companies realize significant competitive advantages, including higher revenue and improved profitability. And corporate boards and c-suite executives expect risk managers to use analytics as part of the decision-making process. Yet many organizations are still not unlocking the full potential of their data and risk analytics.
During our The New Reality of Risk webcast series, we asked participants to respond to this statement: "I am confident that my organization is getting the most out of the data and analytics that we use for risk management." More than 100 risk executives responded:
- 9% agreed with the statement.
- 56% disagreed.
- 35% said they weren’t sure.
With analytics capabilities and tools still being built out at many companies, these results aren’t exactly surprising, but they do point to significant opportunities.
Climb the analytics ladder
Think about analytics as a ladder, with every step bringing greater sophistication and value. At the bottom rung, companies primarily use benchmarking data to compare the pricing, structure, and other elements of their insurance programs to those of their peers. But while benchmarking can add a lot of value for an organization, it is only the starting point.
The next step starts with deterministic modelling, which uses a company’s loss profile, based on individual claims details, to project its average expected losses for a given year. The next rung up – stochastic or probabilistic modelling – creates a view of how likely a company’s losses are to deviate from the expected average.
Taking those three steps leads to risk finance optimization (RFO), which overlays risk transfer options on the results of the stochastic modelling to answer three key questions:
- How much risk can your company tolerate?
- Is your company adequately protected against risk?
- Is your company getting the expected value from its insurance program and other risk management efforts?
Finding the answers to these questions can help risk professionals see how their organizations’ existing insurance programs stack up against alternative structures – for example, different retention levels. With that information in hand, you can determine the best way to structure an insurance program given the organization’s risk tolerance, risk appetite, and cost of capital. And then you can start to integrate risk analytics into more strategic decision-making processes.
The bottom line is that there are compelling reasons – from the potential to increase revenue to improving profitability – to work with your risk and insurance advisers to climb the analytics ladder.
To learn more about the benefits of data, analytics, and technology, listen to a replay of our The New Reality of Risk webcast.