Pricing engines – unlocking opportunity for insurers
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As the year comes to a close, the level of uncertainty continues to rise. From continued ambiguity surrounding Brexit to the recent unrest in Hong Kong, insurers have got their work cut out navigating the constantly changing economic climate alone.
A key component of an insurer’s success is its ability to use data to assess risk, and ultimately the propensity to claim. However, the issue of fairness is high on the agenda, as the FCA continue their investigations into pricing. Insurers need to consider how to balance the need to treat customers fairly, with trying to gain and maintain a competitive advantage.
Exploiting data to improve underwriting results
While big data is nothing new; insurance companies have been relying on large data sets for decades, advances in technology have allowed insurers to collect data from more diverse sources and attribute to individuals at scale. Data analytics enables insurers to derive greater insight from such data, driving decision making in a way that historically wasn’t possible.
In today’s modern insurance market, any personal lines insurer worth their salt is using data analytics to refine their underwriting criteria in an attempt to identify the risks that will return the best loss ratios. This segmentation of customers based on their relative risk is one of the underpinning principles of insurance underwriting and is accepted as being fair.
Disrupters in the marketplace are using data analytics to their advantage, creaming off the low-risk cases and achieving astonishing loss ratios. Enhanced agility is a significant added benefit, with insurers now able to rapidly adjust pricing in response to new trends that they identify.
The consequence is that traditional insurers will be left fighting over the less profitable customers, and if they can’t achieve premium increases to match, their loss ratios will deteriorate as a result, or customers will be left unable to purchase cover.
A pressure to reduce operating cost
One persistent challenge in recent years has been to improve combined operating ratios, as there has always been limited wiggle room in loss ratios alone. Many insurers have taken on the challenge of reducing operating cost through improved processes, leveraging intelligent automation, and through encouraging customers to self serve through digital channels. In some cases, this has meant forcing consumers down distribution and contact channels that may not be their first choice, or the most effective.
How can insurers compete in this landscape? One option is to improve integration between the traditional insurance company divisions - namely a closer working relationship between underwriting, claims, sales and service. The two latter departments have vast reams of data that can be useful for pricing and underwriting, but not just from the traditional loss ratio perspective. At a very simplistic level, the cost of a policy is based on the following factors:
- Cost of acquisition
- Risk of claim
- Value of claim
- Cost to serve
Data analytics is already used effectively in establishing risk and value of a claim, with many carriers leading the way in prediction models. Cost to serve has always been the challenging point, albeit this is a significantly smaller portion of the insurer’s costs. Many organisations have gone in pursuit of lower-cost channels, such as online, or service out of lower-cost locations. The model has been based on reducing the cost of a particular interaction.
What about using analytics to predict and price for the need for the interaction? An alternative approach is to use pricing engines to allow for differences in Operating Expense, depending on the type of customer being insured. Understanding the expectations and key human interactions between customers and their insurer is an important element in pricing for the overall cost of servicing a particular customer’s policy.
Time to think differently – pricing in servicing effort
It’s not new for an insurer to use their pricing model to select its target customers, the logic is quite simple – give preferential rates to those customers you want and increase rates to those you don’t. This is traditionally based on value & risk – but what if it was based on effort? The human experience. People make decisions, and every decision that is made in relation to the purchase, management and maintenance of an insurance policy creates a consequence.
Many insurers already charge for changes to a policy via the application of admin fees. It could be argued that these fees are unfair, as they only appear in the small print of insurance comparison sites, not in the headline comparison data.
Would it be equally unfair to imagine an organisation that selected its customers, knowing to a great degree that they would never make an amendment, or that they never phone, and prefer to interact via an app? For many years people have looked to reduce operating costs through tackling failure demand – demand driven by not getting something correct. What about reducing all demand, by selecting customers who fit your low touch model, or charging extra for those who don’t. Propensity to make contact is just as important to understand as the propensity to claim. In that scenario, the headline price would be the only price to pay, as the insurer could be confident that admin fees would not be required to cover the cost of the additional effort involved.
Possible criteria include:
- Do you plan to move houses in the next 12 months? – and will you therefore make contact to notify of a change of address
- How often do you change your car? – similarly, another contact to change a vehicle
- Do you have children aged over 17? – are they likely to be added on as named drivers
- Do you foresee changing your bank in the next 12 months? – and want to change your direct debit details
Each of these questions provides a valuable data point on how likely the customer is to introduce cost into your system, a view that could be used to price accordingly. And some of these questions might be able to be derived by having access to external data sets such as social media.
This would result in the insurer providing a total price that takes into account the overall predicted cost to serve. This would allow a shift from penalising customers who make amendments or purchase over the phone through higher prices or fees, to a predictive model that makes a decision on the potential policy cost (as opposed to indemnity cost) for an individual.
The outcome could be a customer set that has free choice on distribution channel, free amendments and multi-channel contact – but in a system that costs less because it has all been priced in and volumes predicted.
Alongside demographic data and other, indirect, indicators such as type of phone wrapped together with modern data analytics solutions, we believe vast swathes of customer data can finally deliver on the promise of enabling hyper-personalised policies – with associated higher profits.
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