Maximising insurance efficiency with data and analytics

Continuing pandemic challenges are driving businesses to focus on achieving maximum efficiency. Data and analytics are crucial for maximising efficiency in insurance purchasing.

Continuing pandemic challenges are driving businesses to focus on driving maximum efficiency from every cost line. Insurance is no different: it is critically important for businesses to ensure they are maximising insurance efficiency in terms of protection and cost.

To achieve that objective — especially in the current insurance market environment — there are few who would contest the importance of data and analytics. With the ongoing pricing, capacity, and coverage challenges, compounding financial difficulties as a result of the ongoing pandemic, most organisations are beginning to base their insurance purchasing decisions on more than purely the premiums they will pay.

Many have also redefined their risk tolerance and insurable risk appetite[1], and look to deploy analytics to inform a resetting of self-insured retention levels. But how else can data and analytics inform an organisation’s risk financing strategy? And, for those earlier on in their data and analytics journey, how do you get started?

Moving from historical data analysis to predictive analytics

The beginning of a journey to more advanced risk financing is acknowledging that insurance premium is not the full measure of the cost of an insurance programme. Rather, it is useful to use some form of “cost of risk” metric, which should at least span:

  1. Average retained loss costs.
  2. Premium and taxes.
  3. The less easily measurable costs associated with funding “volatility”.[2]

Organisations can go some way to compiling a historical view of average retained loss costs and premium and taxes based purely on historical data analysis of their own loss experience and paid premiums. With some work to appropriately band — and perhaps inflate — that historical data, an organisation can even form some hypothetical view of their future, expected “cost of risk” for given lines of insurance with differing hypothetical levels of self-insured retention.

A next step would be to factor in claims development and historical changes to base exposures. Further still would involve supplementing an organisation’s own historical experience with losses experienced by industry peers, to allow for the potential costs of historically avoided, but feasible loss events. This moves into the realms of predictive analytics. We now have a cost of risk view that also accounts for the costs of unrealised losses and provides visibility on potential loss volatility.

With these insights, an organisation should more confidently be able to forecast and visualise their retained (and transferred) loss costs under a range of self-insured retention strategies.

Excess layer and insurance portfolio efficiency analysis

However, in many instances, an organisation’s insurance decision-making and/or ability to materially influence premium levels is no longer purely based on optimising levels of self-insured retention for individual lines of coverage. Many organisations are faced with further questions such as:

  • “With insurers seeking to reduce or withdraw capacity[3], should I seek to purchase the same limit of indemnity at any cost?”
  • “If not, how much insurance do I need? Is there a more efficient way to structure my excess insurance layers?”
  • “With a restricted budget for insurance, for which lines of coverage should I prioritise my insurance spend?”

These questions above are perfectly answerable with an appropriate view of higher severity, lower probability loss outcomes, as generated by predictive analytics.

The first two questions above can be answered with analytics to derive a ratio of premium quoted or charged by the insurance against modelled losses forecast to be transferred to a given excess layer of insurance. This will enable organisations to identify the least financially efficient layers of insurance, or even layers that analytics would suggest to be surplus to requirements.[4] They can then decide whether to continue buying that layer of insurance, to retain that risk, or explore ways to restructure the surrounding insurance layers to generate efficiencies.

By comparing the “value”[5] derived from a given line of coverage to other lines of coverage in a client’s portfolio of insurance, it is also possible for organisations to use predictive analytics to determine on which lines their insurance budget is best prioritised.

Organisations will then find themselves equipped to inform advanced risk financing strategies, using data and analytics to identify the following:

1.     The optimal levels of self-insured retention[6], both each-and-every loss and in the aggregate.

2.     Appropriate limit purchasing and efficient structuring of excess insurance layers.

3.     The most “valuable”[7] lines of coverage within a portfolio of insurances, on which to prioritise limited insurance budgets on a risk-weighted basis.

So what should you do?

The journey to maximising insurance efficiency is not completed overnight, but you can start right now. Reflect on whether you currently utilise data and analytics in your insurance decision-making and engage your risk adviser to discuss what level of support is appropriate for your current level of risk financing maturity. Start simple and trust the process, but make sure you are adequately equipped to make ever more complicated decisions around your financing of insurable risk.

[1] Risk Appetite: Altering Philosophies in Uncertain Times

[2] Unexpected loss outcomes.

[3] i.e. The amount of insurance they are willing to provide, measured by means of limit of indemnity.

[4] Which should then be considered in the context of factors such as industry benchmarking; estimated maximum loss calculations; commercial and contractual expectations or requirements; and insurable risk appetite.

[5] Defined as the beneficial reduction in retained losses and volatility that a particular insurance programme provides, in exchange for the premium charged — as suggested by predictive modelling.

[6] Optimal self-insured retentions being defined as those that drive the most financially efficient level of modelled retained loss costs vs. premiums charged for corresponding risk transfer.

[7] Lines of coverage that provide the greatest reduction in retained losses and volatility in exchange for the premium charged — as suggested by predictive modelling.