- Insurers can enhance their customer experience and mitigate risk by using advanced analytics and machine learning algorithms to calculate insurance risk more accurately and efficiently.
- By leveraging data from various sources such as social media and telematics, they can gain deeper insights into their customers' behavior, preferences, and risks, and tailor their products and services accordingly.
- Implementing these solutions successfully requires firms to overcome challenges related to data quality, model accuracy, and regulatory compliance.
The insurance world is always on the move.
Risk models become outdated shortly after they’re created. Some age faster, some slower, but they all end up disconnected from reality eventually.
A good example here is water escape. For years, the number of bedrooms in a house was used to predict the likelihood of flooding. As communities became more prosperous, people started adding bathrooms, either by adapting spare rooms or walk-in cupboards, or by adding an extension.
Move with the times
These new adapted rooms were not designed to be bathrooms and, consequently, were more likely to suffer an escape of water. So, the number of bedrooms ceased to be a valid indicator of potential flooding. If insurers were able to compare today’s number of bathrooms with that of 20 years ago, they would have a more accurate indicator of risk.
While we’re sure that the revenue stream and the increasing number and severity of claims will not go unnoticed by a diligent insurer, the unknown factor is whether any prevention is possible. How can we spot aging models and react early?
Think more creatively
There’s an entire domain of seldom-used policies where the time spent choosing is more expensive than the actual cost of the insurance. Take insuring a single ski ride. With a small amount of data — ski-school spending, the difficulty of the slope and skiing history, for instance — you could determine the risk associated with the skier, then automate the entire process and embed the insurance into a ski pass.
Both occasional skiers and insurers would be delighted. Skiers would pay less for their sport, and insurers would get higher margins on individual trips as the overall policy would be more affordable with fewer concerns.
It’s hard to imagine taking a similar approach to more traditional insurance products as there are regulations already in place. But what if the law or the market, changes? What if disruptors enter the space and offer per-mile motor insurance? Or a disruptor joins forces with car makers and alters the marketplace?
What if food retailers start offering product-related insurance where, by monitoring your shopping basket, they could evaluate the risk of health issues based on what you eat? A fast-food habit would increase the life premium, while buying fruits and vegetables would decrease it.
Build brand loyalty
Small, precise insurance products will prove to be a pain to maintain. Individuals will find it impossible to keep track of all their products. This is already happening. People are finding they have multiple warranties on brown and white goods (e.g., dual cover on phones because their provider bundled insurance with the monthly rental, or their bank included insurance in a packaged personal account).
Browsers are careful about their choice of insurer for expensive insurance products, but what will happen when there are lots of small, easy-to-acquire products? No doubt, people will revert to known brands for convenience. If all insurance companies offer more or less the same products and services, customers will stick with a familiar brand that provides a satisfactory customer experience. They’ll buy other products from that brand too, simply ignoring the competition.
Elevate the customer experience
Naturally, the challenge now becomes twofold. You need to:
- Nurture customer trust by reducing the hassle of claiming, while increasing antifraud efficiency
- Know what to sell to whom and when
The banking and retail sectors (especially in Europe) have already learned how to do that. So, which components do you need to establish a solution like this?
Deepen digital relationships
Some components are well established, but the banking sector is considerably ahead in terms of “open banking” and the delivery of services through apps. The pensions market is moving swiftly toward replicating open banking, recognizing that control of the customer is initially more important than controlling the actual funds. And it’s only a matter of time before property and casualty insurers begin to focus on developing a close, digital relationship with their customers.
Insurance companies need to learn from banks and develop Agile delivery based on accurate and extensive analytic insights. Where this is not available internally, insurers can exploit partners and providers that have experience in both the insurance sector, and the banking and retail sectors, to ensure changes are delivered quickly.
Review all components at a glance
Entry points have many common components, which can be grouped together as in this Wardley map:
One of the patterns that Wardley mapping helps to identify is that efficiency in one area of a map (blue rectangle) enables efficiency in other areas of the map (green rectangle). In other words, a mature and standardized way of handling data can accelerate product development and improve the product-market fit.
Get in touch
This is the second of three blogs looking at the purpose and value of domain-related data to insurers. Several industry experts have been sharing their thoughts, drawing on market experience, tech knowledge and current customer base to draw their conclusions. You can read the first blog: Get more business value from your insurance data deluge and the third blog: Monetize the customer data you didn’t even know you had.
DXC is the leading provider of core insurance technology globally, with over 1,900 insurance customers serviced by over 18,500 professionals.
Luxoft, DXC’s Analytics and Engineering business, is the preferred partner for banks undertaking strategic transformation, and is providing fresh insight into insurance sector analytics. To learn more, contact: email@example.com