While what we see as the fundamentals and benefits of becoming an “analytical insurer” haven’t changed, being one is even more important now because of COVID-19 and its far-reaching economic impacts.
Defining the “analytical insurer”
When talking about analytical insurers, we are first referring to companies that have embedded three key characteristics in their business: a reliance on data and an intolerance of anecdotes in making decisions; the effective compilation of data to present a single source of the facts; and the ability of all decision makers to access granular insight at the point of making a decision. From those foundations, some insurers are moving on to invest in areas that we group under three umbrella sets of capabilities:
- Active portfolio management, and specifically scenario modeling
- Intelligent intervention
- Digital enabled distribution
The incentives for pursuing these attributes nearly always boils down to a handful of drivers – greater agility, rapid speed to market and accuracy of decision making, all delivered at lower cost. The insurers are reducing the analyze-decide-deploy cycle of decision making from weeks and months to days, or hours in some cases – resulting in stronger market positioning, more competitive pricing, slicker operations, increased confidence, cost reductions and a much-improved ability to adapt to changing markets.
As more companies have been persuaded to invest in the benefits over recent years, competition has continued to fuel an analytics arms race. The exceptional economic and market circumstances that COVID-19 is creating only seem likely to raise the stakes, given the likely continuing impact on premiums, business mix, profitability, resources and working practices, not to mention customer experiences that may never revert fully back to their pre-pandemic nature.
The COVID-19 effect: Consider the dilemma facing hospitality or commercial property insurers. An insurer’s hospitality clients are essentially economically inactive, with the prospect that some will never recover. At the other extreme, some manufacturing plants are working flat out in ways that were never anticipated, potentially raising the risk of things like electrical fires or accidents involving tired employees. Understanding the change in both exposure and underlying risk of a given situation is vital at both case and portfolio level. Being able to scenario model differing lockdown and economic outcomes is key to successfully navigating the post-COVID risk landscape.
That’s not to say that COVID-19 is a signal for kneejerk reactions from insurers. Importantly, responding to the short-term pressures and realities that the virus brings to insurers can be compatible with longer-term ambitions linked to agility and pace of operations. For example, enhancing understanding of your portfolio is going to be just as important to insurers’ longer-term fortunes as it is in the short term, and the same applies to most aspects of capitalizing on the opportunities to build from a stronger analytical base.
Here are a few thoughts on how stronger analytics can assist insurers through the COVID-19 crisis, but also create building blocks for longer-term business benefits:
Active portfolio management and scenario modeling
Going back to our hospitality and manufacturing examples, the uncertainty of COVID-19 and the potential new normal it will create could potentially decimate some portfolios and the basis on which they’re priced.
More granular policy information makes ground-up scenario building possible, putting some meaningful number ranges on observed and anticipated trends, and teeing up a whole range of things, such as evaluating what portfolios will suffer most, or even disappear.
See also: How Coronavirus Is Cutting Connections
The recent work we’ve been doing with the Lloyd’s and London market on active portfolio management demonstrates, however, that this is anything but a COVID-19-related issue; the issue is widely seen as critical to longer-term performance and profitability.
Equally, the COVID outbreak has vividly highlighted the opportunity to derive benefits from modeling more widely – say, moving from claims cost to more sector-based analysis using rich exposure data within pricing systems to look at what companies want to do and need to do in their portfolio mix.
The ability to rapidly test hypotheses, and deliver against options, and then monitor and change tack if necessary has already become a backbone of dynamic pricing in personal lines. Real-time scenario modeling can be a similar enabler for underwriting, pricing and claims professionals in the commercial, life and health sectors.
Whether it’s in underwriting or claims, the objective of intelligent intervention should be to deploy the right resources to the situation at hand. This could mean completely automating a process that is relatively straightforward or using experienced teams where complex judgment is needed. Whether adopting a low-touch, volume approach driven from portfolio data or making sure subject matter experts have the right insight available at the right time to make an informed decision, insurers’ data assets make this possible.
The intelligence comes from deploying a more granular approach and, where appropriate, predictive models to support routing and evaluation decisions. Using large loss propensity models to optimize survey and risk appetite decisions and using conversion data insight to prioritize underwriting activity are simple examples of this.
From an automation point of view, it could be about adding granularity to feed a company’s level of automatic underwriting appetite and claims handling. Some insurers use relatively simple decision rules, such as that they’ll automate a risk if it has fewer than 10 employees, or if a claim is of a certain value. Adding additional decision layers (e.g. trade, geography, portfolio context, trust indices, etc.) refines the decision process and allows the safe expansion of automated approaches and lowers costs. At the same time, you get the most from your underwriters and claims experts by allowing them to use their expertise and add value in more complex, individual cases.
The ability to flex the mix between technology and human input is also highly desirable. For example, if a pandemic were to affect a significant proportion of the team, it would be possible to expand the automated or self-service footprint to bridge the gap. Such flexibility can also provide short- or long-term help in areas such as product simplification and cost management.
Digitally enabled distribution
One thing COVID-19 has done is shine a light on organisztions that are better or worse at interacting digitally with concerned customers. In the process, digital capability has become more a matter of reputation as well as a factor in general cost of doing business and customer experience.
Yet, the digital component is only the tip of the iceberg. Below that there are a lot of hidden but hard-working data assets, supporting applications such as products broken into components, the ability to manage channel conflict and active management of cross subsidies, not to mention addressing the widespread challenges of integrating legacy platforms.
The benefits of getting the beneath the waterline on digital infrastructure right are already considerable, and growing outside the personal lines market when Lloyd’s is creating its digital trading platform, when self-service claims operations are making steady inroads, when initiatives are underway to allow brokers to simplify the binding process and when new digital distribution opportunities, perhaps where insurance is part of something else, increase.
Building for the future
At present, it is hard to understand the implications of the new normal, but foundational analytics capabilities should help insurers to not only better navigate that uncertainty but leave them better-equipped for the longer-term fallout and continuing market transition. As part of an insurance future that will inevitably demand more operational flexibility and nimbleness, with digital platforms coming more to the fore, data and analytics and the wherewithal to use them effectively will mark out analytical insurers from the crowd.