Data Science plays an important role in the insurance business.
Insights

Insurers on their way to becoming data-driven companies

How artificial intelligence can revolutionise the customer experience and your product portfolio

If we think of the availability of data as a treasure waiting to be unearthed, then the insurance industry is in an enviable situation: it has a wealth of data on hand that other industries find hard to beat. Yet, many insurance companies are only just beginning to exploit their treasure. In this article we show why it is worthwhile for insurers to actively pursue the path from a data-based to a data-driven company.

Insight in brief

  • Insurances has a wealth of data on hand. Most of the data is unused.
  • In this article we show why it is worthwhile for insurers to actively pursue the path from a data-based to a data-driven company.

From data-based to data-driven insurance company

Most insurers have long since recognised that the companies ahead of the pack are those that are evolving from data-based providers of insurance products to comprehensive, agile service providers with innovative product and service offerings. Data-driven companies generally find such transformations easy. But how do data-based and data-driven companies differ? A company is data-based if its business model has always been dependent on the storage and processing of data. Data-driven companies, on the other hand, use data, analytics and artificial intelligence (AI) in all functions and areas: they use data as a basis to make better decisions, they use AI and machine learning technologies to make processes leaner, faster and more customer-oriented, and they use the possibilities offered by these technologies to develop radically new products and smart services, thereby opening up new sources of revenue.

Probably the best-known example of a data-driven company is Netflix: the company has been offering its customers recommendation systems for media content for 20 years and uses analytics to make fact-based decisions for in-house productions. For example, the concept for "House of Cards" was developed on the basis of analysis results on customer behaviour as well as on human judgement.

New normal, new plans?

Having arrived at the "New Normal", many decision-makers are asking themselves what role the Covid 19 pandemic is playing in digitalisation and data initiatives. The many studies that have been conducted regarding this question all come to the same conclusion: the pandemic is an additional driver of such initiatives.
The pandemic has highlighted the purpose and need for digitalisation and the wider use of data, analytics and AI. Digitalisation trends in the insurance sector are being reinforced by the corona virus. We will now look at the two most important trends, the customer experience and the change in offerings, from the perspective of a data-driven company.
 

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The customer experience as a disruptive force

The interaction of customers with their insurers has changed dramatically over the last decade: today's insurance customers want to receive targeted information quickly, they expect fast reactions, they want personalised, scalable offers, they want to compare them with other offers at any time, and they are usually prepared to adapt their needs to new situations without any hesitation.

In the pandemic, this increased customer expectation has manifested itself in an above-average number of enquiries in the customer centres – enquiries about claims for cancelled trips and events, about premium reductions due to non-use of insured activities (driving, travelling), and even enquiries about the insurability of pandemics.

The demands of today's insurance customers in the area of customer experience – targeted and proactive information, personalised advice and offers, short reaction times – can be exceptionally well implemented with machine learning and AI methods. For example, using systems based on Natural Language Processing (NLP), enquiries to customer centres (via e-mail, SMS, telephone) can be automatically differentiated by topic and assigned to the right employees. At the same time, important information for processing the request is automatically gathered from the text of the request and fed into the insurer's systems. An additional algorithm prepares the customer centre for increased volumes of requests for specific topics ("hot topics", such as the topics described above).

Such systems can be linked as required to other tasks in the customer process: if the customer files a claim, for example, AI-based claim management systems can automatically check the claim for fraud, estimate the amount of the loss and recommend action to the claim handler. As a result, the processing time for a claim can be reduced from days to just a few minutes, massively improving the customer experience. Each interaction can in turn be used to collect data on individual customer needs and thus to further personalise offers and information.
 

Fundamental change in the range of products offered by insurers

In addition to customer interaction, the portfolio of products and services offered by insurers is also undergoing a fundamental change: away from standardised insurance products for claims settlement, towards a holistic portfolio of services and modular, flexible products and smart services that are primarily aimed at loss prevention. Customers are increasingly opting for a particular insurance provider because it offers them services and tools that are not directly related to insurance risks but make their lives easier and more pleasant.

The data-driven insurance company has a clear advantage in the innovation of such new services, products and tools, since the AI/ML technologies it masters provide an excellent technical basis for developing radically new offerings. For example, based on machine learning, the risks of individual, time-limited customer activities can be calculated so precisely that data-driven insurers are now able to offer customers ad hoc, tailored insurance policies, e.g. to insure expensive photographic equipment for a planned weekend trip to New York.

Various data-based products and services are already offered in the area of prevention. One of the best-known examples is the "Drive Recorder", a system that records and analyses the driving behaviour of motor vehicle insurance customers (known as time series analysis) and rewards cautious and anticipatory driving with reduced insurance premiums.

Insurers are also increasingly using the visual, acoustic and other sensory capabilities of smartphones to make their customers' lives safer, healthier or less complicated. For example, apps based on Computer Vision allow customers to take photos of their daily meals and easily record and analyse their personal nutritional behaviour. The app can give its user recommendations for a healthier diet or for following a diet. Staying in the area of disease prevention: using acoustic analysis methods, for example, it is possible for customers to use an app to examine themselves for possible respiratory diseases, such as sleep apnoea.

With all these ideas it is important to continuously check whether the resulting solution meets a real customer need. Experience shows that solutions that are mainly driven by technological possibilities have a lower chance of success.
 

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The path to a data-driven insurance company

This makes it easier for data-driven companies to implement and use the trends described. So, what should an insurance company do if it wants to develop into a data-driven company? Zühlke has developed an appropriate process model from its more than 10 years of experience with data and Artificial Intelligence projects. It consists of three main steps:

The Zühlke process model for data-driven businesses
The Zühlke process model for data-driven companies
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In the first step, companies prepare themselves for the transformation. A leading core team, in which top management must be represented, first develops a vision that explains why the organisation wants to become a data-driven company. It is important that this motivating vision also addresses and dispels any fears or potential resistance from the workforce.

The "what" is the second step: starting from the corporate strategy, a data strategy is developed. This is operationalised by developing an initial portfolio of concrete projects and initiatives.

Finally, in the third step, the foundations (capabilities, technical data platforms, structures and processes, etc.) are created and projects and initiatives are implemented in parallel.
This is the central functional principle of our process model: foundations are created step by step and always in relation to a concrete implementation project. This ensures that practical and lean structures are established, which can be tested and, if necessary, adapted based on a concrete use case. For the first implementation project, an application case is chosen that has a good chance of success. This "lighthouse project" should have a positive impact on the company and thus further strengthen the internal acceptance of corporate transformation.

The procedure shown represents an agile, practice-oriented and field-tested way to establish the data-driven enterprise. Within 4-6 months (depending on the scope of the first implementation projects) the foundation for a data-driven insurance company and thus for an innovative, future-proof insurance business can be laid.
 

Philipp Morf

Philipp Morf

Director Solution Center
Contact person for Switzerland philipp.morf@zuehlke.com +41 43 216 6588

Dr. Philipp Morf holds a doctorate in engineering from the Swiss Federal Institute of Technology (ETH) and holds the position head of the Artificial Intelligence (AI) and Machine Learning (ML) Solutions division at Zühlke since 2015. As Director of the AI Solutions Centre, he designs effective AI/ML applications and is a sought-after speaker on AI topics in the area of applications and application trends. With his many years of experience as a consultant in innovation management, he bridges the gap between business, technology and the people who use AI.