- The insurance industry has access to vast amounts of data that can be leveraged to gain insights and create business value.
- However, many firms struggle to extract meaningful insights from it. Data needs to be integrated from disparate sources such as customer interactions and social media.
- Implementing advanced analytics techniques such as machine learning can help insurers unlock the business value from their data by enabling more accurate predictions and personalized services.
It will come as no surprise to hear that CEOs and CIOs receive masses of promotional material covering the latest and greatest developments in technology.
Some items explore things like how to use smartwatches to gain a competitive edge. Others are about building car-tracking devices and using the data to better-price the risk, all the while championing a safer driving style or using automation to simplify the offering — removing the need to make a claim even.
It’s only a matter of time before these small-scale experiments lead to a breakthrough in the relationship between customer and insurer, causing a near-revolution that will mean the end of the road for companies unable to adapt.
Handling data at scale
We’re not trying to tell you how to run your business, but organizations are creating and storing more data than ever. And significant modern terms such as automation, AI and customer 360, all require an ability to handle data at scale that, previously, was never an issue.
This is nothing new, though. Insurance has always been a data-handling business. But what is new, is the scale of this phenomenon.
Exploiting industry trends
This is the first in a series of three blogs covering insurance industry trends, and what your company needs to do to capitalize on them.
Most of the trend ideas have been with us for years one way or another, but ease of use is the new black — a fresh slant makes all the difference. Two decades ago, building a customer base or meeting regulatory requirements were tedious tasks and, while competition was tough, the market was more or less closed to new players.
Generating added value
Today, the situation is much different. There are lots of global and local organizations generating huge volumes of data and trying to figure out how to use it to deliver more value. Providing insurance, explicitly or not, is one of the attractive directions for adding value to existing products. So, insurance companies face some tough choices. Should they respond? If yes, then how?
There are no easy answers, particularly with the enormous pressure to be more efficient in a traditional way without actually changing the nature of the business done. It will work until new market entrants disrupt the market, segment by segment.
Winning the broker game
Insurance price comparison sites are profitable as long as players have similar risk models and use a common set of more-or-less standard questions. Change is unwelcome because it generates work — new questions and workflows need to be implemented, and the comparison site operator might not be willing to do that just for one provider. And even if the work is done, customers may not be willing to answer a single question.
The overall situation stops insurance companies innovating and, so far, the only way forward has been to establish a better brand-customer relationship. There is another way forward — augmenting the data that the customer has typed in with the data acquired from different sources in real time.
Making fact-based decisions
For instance, instead of asking where a car is usually parked, you could use data obtained from the owner’s mobile phone provider to identify the driver. Then, assess how well he or she drives the car, based on their Bluetooth black box. The availability of this kind of information would reduce the administrative burden on customers in many situations and, even if it didn’t, it could help insurers identify dishonest customers.
There are a few components that could be implemented to enable this sort of operation.
Figure 1: Which components do you need?
Reviewing data ethics
Clearly, many of the necessary components are in place — data streaming, lakes, platforms, APIs and so on. Cloud and connectivity are readily available too. However, you might need help creating the mechanisms for collecting and integrating the data, and you’ll need to consider the ethics of controlling and using the data.
This might mean building proof of concept (PoC) models and minimum viable products (MVP), which could be accelerated by using an appropriate InsurTech firm and working with modern Agile technology experts who have experience in the sector.
Applying the aggregator model
In future, the crossover into wider industry will become more apparent. It will be possible to open the same APIs that aggregators use for other commodity-type providers (carmakers, smartphone producers, airlines, etc.). Indeed, the rise of food distribution businesses like Deliveroo is bringing the aggregator model to food shopping too.
The insurance sector has ceded a lot of control to the aggregator, and is trying to regain that control just as the retail sector is beginning a slow waltz with a new type of aggregator. The key question for a retailer entering such an arrangement would be, “is my brand strong enough to retain market share, and does quality become a lower driver than price?”.
Sharing expert opinion
Various industry experts contributed to this three-part series of blogs, drawing on market experience, tech knowledge and current customer base to draw their conclusions. You can read the next blog: Calculate insurance risk more precisely and turbocharge your digital customer experience 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