Feb 11, 2022
Telematics, which relies on Big Data analytics, enables precise pricing and can enhance the underwriting process. However, there is still disagreement among insurers about what portfolio management should look like.
AI-based risk analysis drives car insurers forward
Telematics, which relies on Big Data analytics, enables precise pricing and can enhance the underwriting process. However, there is still disagreement among insurers about what portfolio management should look like. What needs to change in terms of methods, communication, and transparency.
Modern traffic telematics should make car insurance simpler and fairer. Car insurers save money by calculating risks precisely. Insureds receive customized offers and are rewarded for prudent driving with premium discounts. And mobility, in general, will become more efficient, more ecological, and safer. So much for the theory. The practice, however, looks somewhat different: Underwriters have difficulties processing the additional scoring parameters from telematics profitably and implementing the modern form of risk assessment in their portfolio management. Insurance customers are confused by the overabundance of new solutions, do not see any advantages in the offers, especially in connection with big data, and are primarily concerned about their own data security.
Telematics is a magic word that has been haunting the insurance industry for years. The savings bank subsidiary S-Direkt offered Germany's first telematics motor insurance model back in 2014. Subsequently, other providers came up with their own solutions. In the meantime, every major motor insurer has a corresponding tariff on offer. Nevertheless, no concept has proven to be successful across the board. Thus, many of the early products have either been discontinued or replaced by alternative models. Despite setbacks and technical inconsistencies, however, telematics is still considered the most important megatrend in motor insurance alongside autonomous driving. And not without reason.
Mobility is changing worldwide. The main reasons for this are advancing digitalization and the shift in traffic towards electromobility and autonomous driving. The technical development is so rapid that insurers can hardly keep up with their products. That is understandable,
After all, they are about fundamental changes in risk analysis and tariff regulations. Suddenly, vast amounts of additional and sometimes completely different data are available from sources that were not previously taken into account in a calculation. Digitalization always means individualization and is initially associated with additional work for insurers.
Traditional risk assessment factors, such as claim-free years, vehicle type, and personal customer data, have so far provided a clear but also a static picture of policyholders. Risk analyses based on the latest data science methods and supported by big data are shaking up this foundation. An individual risk assessment is created from dynamic and continuously updated data, which is based on a wealth of factors.
More precise risk analyses promote customer transparency.
Relevant and clear benefits increase the attractiveness of the offer.
Cooperations with data analysts push quality.
CENTRAL DATA SOURCES OF TELEMATIC MOTOR INSURANCE COMPANIES
GeoData - location of the insurance customer
IoT - location of the insurance customer
Big Data - location of the insurance customer
First-Party-Data - location of the insurance customer
The digitalization of motor insurance is therefore not a further development of classic risk analysis, but a huge upheaval. The potential for the insurance industry as well as for society is gigantic because data-driven analyses are more accurate and thus fairer than the classic classification into fixed categories. In addition, insurers can significantly improve their combined ratio through optimized portfolio management.
Current tariffs often prone to errors
But until the benefits are felt, key problems that the industry continues to struggle with are the need to be addressed. Complaints to consumer centers point to the fact that current telematics tariffs are prone to errors. For example, road users who are dependent on night driving due to shift work or who regularly drive through areas with high traffic density may be at a disadvantage. These circumstances are considered negative factors. In addition, there are negative points that insurance customers associate with Big Data. With early telematics models, for example, drivers had to install a black box in the car, which many perceived as a foreign body in their own car. In extreme cases, drivers feel monitored every time they look at the black box in their own car. Basically, the lower the personal effort, the more positive the product perception.
A particular problem with telematics is data protection, to which special attention is paid in Germany. ermitteln den Standort des Versicherungskunden. ermitteln den Standort des Versicherungskunden. Some customers are skeptical about whether their data will be used properly, mostly out of insecurity. But these concerns are unfounded. After all, insurers have always handled personal data in a routine manner, and of course, they also have to comply with the General Data Protection Regulation (DSGVO). Thus, consumers' fears are a prime example of a phenomenon known as the Privacy Paradox. On the one hand, digitalization is viewed critically and fears of data misuse are expressed, but on the other hand, practical innovations are welcomed and accepted uncritically.
The smartphone, for example, constantly collects and delivers personal data to companies, but hardly anyone considers this a problem. Those who jog through the forest during marathon training rely on GPS tracking and thus cannot easily get lost in the wilderness, but at the same time usually, give little thought to data protection in the process. In general, customers tend to view constant data analysis during the journey negatively. However, this rejection is also paradoxical, because modern vehicles constantly retrieve geodata even without a telematics box or app. For example, new vehicle models have had to be equipped with the automatic emergency call system E-Call since March 2018. However, the driver hardly notices this.
Ultimately, the personal advantage is decisive for a positive or negative customer rating. If the customer realizes that he or she has personal benefits from a product, for example through the automatic emergency call of the system in the event of an accident, the data protection concerns are noticeably reduced. So if the customer's own data release is linked to a verifiable benefit, the skepticism sometimes disappears quite quickly. And if the insurer is not just a reimburser of costs, but also takes on an advisory function, the image of modern tariffs is also likely to improve.
Insurers and intermediaries should adopt these findings and clearly communicate the advantages of big data in the context of risk analysis. If an insurance customer has internalized that a product can benefit him financially or that the probability of an accident is significantly reduced with such a product, acceptance should increase noticeably. In concrete terms, this means that insurers and intermediaries should emphasize advantages for their customers as clearly as possible: How much money can be saved? What do concrete improvements in driving safety look like for the customer? And which developments in general road safety or even road efficiency will concretely benefit the customer?
Another point that can lead to more acceptance of telematics insurance products lies in the weighting of the data. Therefore, providers should think carefully about the concrete benefit to be gained from the collected information. Numerous statistics state that accidents happen at certain traffic points or in certain weather conditions with both low-risk and high-risk drivers.
For the reasons mentioned above, it would make sense for modern offers to focus less on the origin and age of drivers and instead focus primarily on influences such as weather, road conditions, or brightness. In this way, the customer will also realize that it is not about collecting personal data. For insurance customers, the road safety factor should be convincing, while actuaries can use suitable data to create insurance products that are as tailored as possible.
In order to present uncomplicated and customer-oriented solutions, however, technical knowledge is required, which not only actuaries should have. Even though the insurance industry has always worked with a wealth of data, obtaining it is not part of its core business. When it comes to big data, the biggest innovations in recent years have not come from the insurance industry.
Modern IT solutions that rely on a plethora of different factors should be developed in cooperation with, but not necessarily by, the insurance companies themselves. Specialized providers of automated claims assessment technology use artificial intelligence (AI) and deep learning technology that automate and streamline insurers' portfolio management. In other words, without cooperation, the consistent development of telematics will not work.
The future is all about autonomous driving, which will bring another upheaval in the field of mobility. Away from the debates about data protection and premium models, insurance tariffs that rely on dynamic data processing also offer enormous potential for this. In general, the use of risk assessment algorithms leads to efficiency gains that the industry will not miss. And the exponentially growing data volumes of the future fundamentally demand professional interpretation, which insurers can provide in cooperation with traffic data analysts.
As soon as the insurance industry comes forward with realistic and customer-oriented solutions and clear added values reach the customer, targeted tariffs with the support of big data and complex risk assessment algorithms are likely to become much more popular. This effect could intensify as insurers transform from acute problem solvers to active problem preventers, and positive customer experiences have a snowball effect. Once data-based insurance products have anchored themselves in the customer's consciousness as positive, nothing should stand in the way of the triumphal march of custom-fit motor insurance. Because with more precise and, above all, comprehensible offers for insurance customers, trust in AI-based decisions should also grow.