What does big data mean for insurers

Big data holds potential for insurance companies

Most insurers are currently unable to implement many use cases in the area of ​​big data because they do not have sufficient data and the necessary data quality. In order to change this, you have to do important homework. Brokers should not be forgotten, especially since they too can benefit from big data.

The amount of data generated worldwide is growing by around 30% annually. Responsible for this is the increasing number of data-collecting devices, for example in the areas of smart home, telematics or e-health, but also numerous smartphone apps.

The large American digital corporations in particular benefit from the fact that many users willingly reveal their data in order to be able to use services and apps free of charge. The data is used to present targeted advertising, to offer additional services or to resell the data in processed form. But manufacturers of medical and fitness equipment, consumer electronics and vehicles have also become data collectors thanks to the “Internet of Things” (IoT) and use the data to expand their business model.

But while big data is already a reality for digital corporations and increasingly also industrial companies, insurers lack the basis for big data, namely the data. The main reason for this is that the classic business model of the insurer generates very few customer contacts and therefore little information can be collected. With the exception of the annual collection of premiums, a customer has no interfaces to the insurer, apart from claims settlement in the event of a claim. There is no lack of suitable use cases. With customer data, for example, marketing and sales can be optimized, risks better assessed or processes along the business transactions in the areas of application, contract and damage or benefit can be automated and accelerated.

Additional services for more data treasures

Big data therefore begins for insurance companies with the creation of additional contact points in order to obtain information about the behavior and wishes of customers or the status of insured objects. Most insurers already provide a rather sparsely used online customer portal with which customers can view and manage their existing contracts and, if necessary, also conclude new contracts. It makes sense to expand this portal as a platform on which additional services are offered. In the first step, the additional benefits could be more closely related to insurance, for example tips on fitness and health or safety around the house. In any case, the offer must offer the customer a clear added value in order not to quickly belong to the vast majority of apps that are installed but only used once.

Another way of receiving data from customers is to expand the product portfolio with additional services based on IoT. Smart home household insurance, telematics tariffs or health programs that use e-health information can already be found on the market. Once such an ecosystem has been established, the offer can then be expanded to include additional services, for example in the areas of mobility, health services or household-related services. However, insurers cannot offer such services on their own; cooperation with companies from the telecommunications, automotive and health sectors is required here. Finally, customer information can also be purchased from personal information providers such as credit reporting companies.

Diverse potential beckons

But ultimately, insurance companies make the effort, as they can use the data for useful analysis. Classic analytics application areas are marketing and sales with customer segmentation, customer value analysis and the determination of cross- and up-selling potential. Customers who have a greater need for security based on big data analytics results can be offered more extensive policies or more expensive tariffs. With additional data from the customer, business transactions in the application and existing business can also be optimized. For example, on the basis of an applicant's health data, the risks of illness can be assessed more reliably and individual pricing can be carried out automatically. In addition, the cancellation rate can be reduced if termination risks are identified in good time based on payment behavior or creditworthiness.

Some insurers already use analytics to detect insurance fraud or in claims management. Using photos of the damage, the amount of damage or the current value of an object can be determined and the customer can be offered quick claims settlement, especially in the case of minor damage. Information obtained from big data can also be used by insurance companies to improve the assessment of risks and thus to carry out risk-adequate pricing. Examples of this are motor vehicle tariffs that take customer driving behavior into account, or bonus programs for health-conscious behavior.

Hurdles and homework

However, there are limits to these approaches, as the risk-adequate pricing is considered as part of the profitability review by the insurance supervisory authority. Ultimately, the calculation must be comprehensible, which is difficult to do with some statistical analytics methods. Further regulatory restrictions result from the provisions of the GDPR, which limit the options for storing and using customer data.

In addition, insurance companies have to do a lot of “homework” before they can actually use big data. In the past, insurers have usually managed their data in silos and poor quality. There is therefore a lack of data architecture concepts that enable comprehensive networking and usability of the data. To make matters worse, there is currently a shortage of experts on the market who also have expertise in common analytics and AI processes.

Include intermediaries in data strategy

An important and reliable partner in connection with customer data is mostly forgotten: the intermediary. Ultimately, he knows the customer best because he has built a personal relationship with them. The information in the agent's customer contact history is therefore at least as valuable for analyzes as the data from the fitness app or telematics application. The problem, however, is that insurance agents and even more so brokers have little interest in making their customer information available to the insurer, as they form the basis of their business model. And even if intermediaries provided their data, they would usually not be available in a standardized form, which makes it difficult to use for analytics.

A big data strategy must therefore always include the intermediaries, because they can benefit just as much from the use cases in the area of ​​CRM and sales as the insurance company itself. Even if the intermediary knows the customer well, a suitable recommendation for “next best action “increase the sales success or give indications that the customer may want to terminate the contract and thus the inventory commission is at risk. If the intermediaries recognize added value from big data in that they receive valuable information from the insurer or processing processes are accelerated, they are more willing to participate in the information sharing. The word that data is the new oil has already got around among intermediaries. Insurers should avoid intermediaries selling it to InsurTechs or digital corporations.

This article was first published under the title "Insurance and Big Data: Every beginning is difficult" on November 6th on the AssCompact homepage and in the print edition 11/2018, page 94 f.

Dr. Matthias GröbnerDr. Matthias Gröbner is a Detecon partner and advises insurance companies and banks on enterprise architecture management, InsurTech / partnering and digital business models.

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