By Andy Smyth, Head of Strategic Risk Consulting and Sobia Sheikh, Director of Enterprise Risk Consulting, WTW
While over-insurance can represent poor resource allocation, under-insurance can significantly undermine resilience and long-term success. In this insight, the second in our risk and insurance optimization series, we look at using analytics to make the right savings on risk lines. With nuanced, data-driven risk insight, you can make insurance savings without leaving your organization vulnerable, and without leaving the standing of risk management exposed.
Why risk analytics can help you save with more confidence
To help you get the right risk retention, insurance limits and deductibles, analytics provides a comprehensive view of your risks and their interdependencies. It’s these perspectives that allow you to strategically adjust your insurance portfolio based on market conditions and ensure that you’re not overpaying for certain lines of business and properly protected against potential losses.
Analytics lets you see where insurance lines are cheaper, meaning you can decrease deductibles or increase limits to take advantage of lower prices.
Analytics lets you see where insurance lines are cheaper, meaning you can decrease deductibles or increase limits to take advantage of lower prices. With an analytical view on more expensive lines, you can consider retaining more risk by increasing deductibles or lowering limits. A data-driven view of your risk lines means you’re able to better balance cost and risk and avoid taking a gamble on changes to insurance structures.
Without analytical insight, you can too easily leave your organization facing inadequate protection against potential losses and missed opportunities to make the most of market conditions.
Key steps to reviewing risk lines using analytics
Identify your key risks based on premium spend to help you focus on those areas with the greatest financial impact. For example, if you spend a large portion of your premium on property damage and business interruption (PDBI) coverage, this should be one of your main focuses or potentially the pilot project before applying risk analytics across multiple lines.
Quantify the risk and dependencies between different lines by analyzing historical loss data, market trends and the potential financial impact of various risks. You might find, for example, that property and liability risks are highly correlated, meaning you should adjust your retention and limits to reflect this.
Evaluate alternative insurance structures to find efficiencies by experimenting with adjusting deductibles, limits or using a captive to optimize capital allocation. Let’s imagine you identify high-frequency, low-severity risks that are overpriced in the commercial market; a captive might be a viable option to manage these risks more cost-effectively.
Make sure you can implement your optimal insurance program before presenting it to wider stakeholders. If you need buy-in from the likes of treasury or finance before proposing adjustments to insurance policies, your approach to setting aside reserves or investing in risk mitigation measures, you’ll need to know the changes required to optimize are achievable. Working with an analytical broker with access to placement specialists means you can take your recommendations to finance and strategy with confidence.
Continuously monitor your risk lines using analytics to make sure your risk and insurance optimization approach remains effective. Because it is typically dynamic and forward-looking, risk analytics can help you identify emerging risks and trends, allowing you to take proactive measures to mitigate them. Ongoing analytical review may, for example, alert you to increasing cyberattacks in your industry, enabling you to advocate for change in your cyber coverage to better protect your organization as threats emerge.
How risk and insurance optimization helped a global manufacturer reduce costs
A global manufacturer faced a significant challenge in its insurance portfolio, primarily due to their premium spend being heavily dominated by expensive PDBI coverage. Its distribution network looked to have substantial exposure to natural disasters, especially earthquakes. This was down to the company attributing a large portion of its profits to its head office, which had high earthquake exposure, leading to an overestimation of earthquake risk and consequently, over insurance.
By developing a sophisticated actuarial model for its PDBI coverage, the manufacturer could attribute profits more accurately to each of its global plants, ensuring the coverage was better aligned with the actual risk exposure. The model also highlighted the premium share paid to its lead insurer was disproportionately high, facilitating negotiations with the lead and other insurers, allowing the business to secure more favorable terms and lower overall insurance costs.
These strategic and practical changes led to cost savings and a more efficient allocation of insurance spend, enhancing the manufacturer’s financial resilience. Want to understand what risk and insurance optimization could do for your financial resilience? Get in touch with our risk and insurance optimization specialists.
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