Many risk managers now understand how data analytics and sophisticated risk modeling are powerful tools capable of driving substantial cost savings across risk and insurance programs.
While the right analytics can inform more efficient risk and insurance program design and help you negotiate better terms with insurers, securing the buy-in, and also the budget you may need to finance the investment in advanced risk analytics tools, may not always be straightforward. You may need to overcome organizational reluctance and financial constraints.
To help you make a compelling case for risk analytics, in this – the first of our risk and insurance optimization insight mini-series – we share five ideas on overcoming internal barriers to using and budgeting for risk and insurance optimization.
01
If business leaders point to existing benchmarking efforts as the reason there’s no need to move to risk analytics, you’ll want to help them understand why benchmarking alone isn’t enough.
While it may offer a useful comparison of your premiums against industry standards, benchmarking can’t capture the specific risks and complexities of your assets, operations and supply chains within your own organization.
Advanced risk analytics offer your business customizable insight by calling on sophisticated data models and machine learning to identify the most relevant risk areas. You can pinpoint exactly where to allocate resources more effectively, and check your insurance coverage isn't only adequate but also cost-efficient.
Benchmarking is also static and backward-looking, relying on historical data that’s unlikely to reflect current market conditions or emerging threats. Advanced risk analytics helps you stay ahead of changing risks and make proactive decisions because they’re dynamic and forward-looking, continuously updating to reflect real-time data and shifting conditions.
02
Does your captive have excess capital it could allocate to strategic initiatives like advanced risk modeling? Getting a more precise, forward-looking understanding of risk with the aim of making your risk and insurance work harder can benefit both the captive and the parent company.
You can present this approach as not only having the potential to reduce the financial burden on your main budget, but also aligning with your captive’s core mission, which will be about managing and minimizing risks more effectively.
03
Your insurer may offer risk management bursaries provided to your business to develop or enhance their risk management capabilities.
This can include funding risk management tools and systems, including risk analytics. A bursary can part or fully fund your first risk analytics project, which ought to yield tangible results that inspire more confidence and ongoing commit to data-driven risk and insurance optimization.
04
Your business leaders will undoubtedly be familiar with organizations using data to drive out inefficiencies. Can you extend their understanding by sharing success stories of where industry peers are applying the same data-driven approach to risk and insurance?
Sharing case studies or examples of where other businesses have successfully used advanced risk models to achieve these outcomes, can help build a strong case for why your company should follow suit.
For example, probabilistic loss forecasting and risk and insurance optimization analytics helped one food and beverage company expose it was making unwarranted investments to mitigate against climate risks the business didn’t fully understand.
05
Securing buy-in from internal stakeholders, such as your treasury and finance leads, is crucial for any major investment. However, as these departments can be focused on short-term financial metrics, they may be skeptical about investing advanced risk modeling, even if you’re able to make the case for long-term value.
To overcome this, try sharing how the underwriters setting your premiums are already likely to be using sophisticated risk analytics to do so. This is about demonstrating advanced risk analytics are not an outlier or theoretical concept but a practical tool already being used to achieve better outcomes, in this instance on pricing risk profitably.
Can you challenge internal stakeholders on why you shouldn’t be going toe-to-toe with insurers’ on analytics?
You could also make that point that you’ll be able to incorporate more nuanced data from the business than underwriters, data that could put you in a stronger negotiating position, drive more value and create better insurance outcomes. For example, your own risk analytics might incorporate risk engineering reports on key manufacturing sites that could demonstrate insurers have overestimated flood risk and need to price your risk more competitively.
Find out how you can save on insurance spend while better protecting the business with risk and insurance optimization. Get in touch with our risk and analytics specialists to find out how you can optimize your risk spend today.
