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New data sources/devices are transforming traditional insurance models: Capgemini

By Rob Starr, Content Manager

Insurers are experimenting with various ways to harness new data sources to improve everything from pricing and claims settlements to risk selection while providing more customer-centric products. Traditionally, the industry has not collected much new customer data during the course of an insurance policy. However, with the industry’s increasing  ability to analyze and understand more data, they are preparing themselves for an uptick in customized products and services powered by big data analytics and predictive modeling. Tony Almeida, Principal, Head of North America, Insurance Analytics at Capgemini Financial Services answered a few questions via email for us.


Why haven’t insurers traditionally collected much new customer data during the course of an insurance policy?

Tony Almeida

Tony Almeida

o Insurers collect data on an in-force policy if that data is provided by the insurer e.g. change of address, or life event. However, such data often doesn’t propagate across all the different departments nor it tracks the date/time stamp of the change. There are a number of reasons why insurers have not proactively collected data on an in-force policy:
1. Product-centric approach without processes to address the insurer’s journey.
2. Agents assume an automatic renewal, and often don’t contact insurers in advance of the renewal date hence losing sight of the customer’s needs.
3. No automated process to link any structured or unstructured data inputs to the existing customer databases.
4. Lack of integrated view of the customer across all departments: actuarial, regulatory, underwriting, distribution, claims.
• Why are insurers experimenting with an ever increasing number of ways to harness new sources of data now?
o Market making for insurers in both Personal and Commercial lines is highly competitive. To sustain the revenue growth demands to further increase the expense to revenue ratio will require the need to understand customer better.
The inherent need to become customer centric demands additional credible customer data integrated with existing data. Additional data sources enable also the ability to create more effective segments with associated well understood risk. This customer knowledge also enables the creation of more competitive products for the appropriate segments hence looking to the customer as more personalized ones.

• How and why are wearables becoming important?
o Wearables are in essence the IoT of consumers. Fitbands and connected household appliances will become more and more popular with consumers as they become commoditized mostly in advanced economies over the next 24 to 36 months. Price drops in fitbands alone will lead to the novelty to become more appetizing for consumers. As consumers adopt, insurers have an opportunity to collect more data.
The insurers’ opportunity will be to acquire more and more unstructured data with in-force policies making them more customer-savvy. Conversely these wearables heighten risks of privacy, confidentiality, and safety. The health and wellness sensitive data will provide insurers the opportunity to provide better managed risk products to consumers but also opens the door capegeminifor this data to become a consumer worrisome embarrassment they may not want to share.
As to consumer appliances the opportunity is different and more effective as consumers may be able to get better intelligence from insurers on what service and/or appliance to buy from whom, as insurers become comfortable and knowledgeable of the data points coming from those appliances as a result of claims for instances.
• How is the Internet of Things (IoT) affecting the insurance space?
o In advanced economies the data acquired from IoT devices for Personal and Commercial lines will be significant. Over the next 5 to 10 years this explosion of devices will open new data services markets assuming large-scale adoption occur. Adoption is inherently dependent on the actors involved to have the technologies, organizations, processes, and policies in place.
There will be another dependency: collaboration among suppliers, consumers whether individuals or businesses, insurers, and regulators. Standards to exchange data as well as regulation similar to what was done with HIPPAA will add to the cost of processing the data produced from IoT devices.
Insurers will need to adopt and be on the forefront so to benefit from a more customer-centric view, understand and manage the data to create more effective lower risk products for each customer segment.

What are some of the challenges and concerns these connected devices and new data sources are presenting?
o These devices will provide an inordinate deluge of unstructured data. While there is the obvious challenge around privacy, there will be the need for insurers to have an approach and method to:
1. Sourcing
2. Integrating to an existing data warehouse environment
3. Ability to model and analyze unstructured and structured data
4. Discovery and decision on which data to keep
5. A robust IT maintenance and update process

While new technologies enable the ability to acquire data galore leveraging the cloud it also is impacting how insurers staff going forward their business and IT teams. Transforming these teams to a new era of skills set as well as understanding the sun-setting journey of legacy applications requires a methodic and rigorous transformational journey.

The inherent investments have more medium and long-term benefits than short-term. However, insurers may discover quickly that areas like distribution and claims may quickly produce a short-term benefit as to more production and better understanding of the claims analytics.

• What’s in the future?
o There are some key disrupting factors that will require insurers who want to be in the leading capegeminiedge in the next 5 t o10 years to get in front of:
1. IoT commoditization including consumer wearables and its impact on how products are used, lifestyles, and data acquisition
2. Regulatory issues around the leveraging and dissemination of the data acquired
3. Preparation to be able to leverage this deluge of data to have intelligent competitive advantage
4. Nimble adoption at the people, organization, and process to become a more customer-centric organization.

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