Data Pools

Correct data is the foundation of pricing. In accordance with the "law of large numbers", market leaders often have an advantage due to a more precise knowledge of risks.

Meyerthole Siems Kohlruss (MSK) operates data pools for various lines of business and countries with the aim of becoming the "statistical market leader" in each line of business. The insurers participating in a pool generate joint statistics that are used, among other things, for pricing, analysis of claims experience, actuarial controlling, forecast calculations and individual risk assessments (predictive analysis).

The data of the participating insurance companies are processed there with well-established processes in an annual cycle.

Participants have access to the "data treasure" comprising many millions of data records via the intuitive and high-performance Excel-based analysis tool "Analyzer". In addition, most of the data pools also provide anonymized and cleansed individual data sets, which give the respective actuaries full flexibility in their modelling.

In addition, Meyerthole Siems Kohlruss (MSK) provides a variety of benchmarks (for example on loss ratios and average premiums), regional zoning models and non-binding net rate models (risk models) every year as part of the service.

In addition to the figures, there is plenty of opportunity to discuss the results and exchange information on current topics and trends at the annual joint presentation of results, an individual presentation of results for each insurance company, and at other exchange meetings on board, user or working group level. External know-how also flows into the data pool through a reinsurer as sponsor of the data pool and through presentations by insurtechs.

Conformity to antitrust law and data protection is guaranteed.

Non-life Data Pool (Private Lines)

Statistics like a market leader

The non-life data pool for private lines has the size of a top 5 company (regarding all lines). The following lines of business are considered: private liability (private liability, pet owners' liability and others), accident as well as householders’ comprehensive insurance and homeowners’ comprehensive insurance. Cross-sector analyses are also carried out as a "customer view".

The added value compared to GDV’s statistics (The German Insurance Association, GDV, is the umbrella organization for private insurers in Germany) includes a number of additionally recorded characteristics such as the premium or the distribution channel. New ideas for products and pricing can also be evaluated via the data pool: A consideration of no-claims discount classes, special target group concepts (e.g. for senior citizens) or the calculation of a square-metre-based tariff for non-life insurance have already been extensively explained in the pool.

This is flanked by regular analyses of the loss reserves at individual loss level. For pricing in accident insurance, Meyerthole Siems Kohlruss (MSK) has its own disability degree statistic.

As of late, participation in the non-life data pool is also possible with just the accident line of business.

The pool is sponsored by SCOR Reinsurance.

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Non-life Data Pool (Commercial Lines)

Identify risks, expand potential

This data pool includes the lines of commercial liability insurance and commercial property insurance (commercial buildings insurance, commercial contents insurance, business interruption).

High-quality data validation enables companies to improve their own data quality down to the individual risk and sub-risk level.

The commercial data pool provides a comparative view of new business and target segments as well as of the portfolio that has been maintained over many years. Targeted data analyses – for example by region or trade group – open up new horizons for product development and the adequate pricing of risks. Using the benchmarks, participants can check in different segments whether they are more profitable than other insurers.

Statistical models evaluate entire types of operations and risks as well as the individual risks of your portfolio and quantify them with a loss expectation.

With the evaluation tool, you can evaluate the reporting year statistics and settlement statistics yourself, including premiums and loss ratios, according to more than 100 descriptive characteristics.

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Property/Casualty Data Pool in Austria

The pioneer for Austria

This is the first pool for property and casualty insurance in Austria. Since 2010, the sections of household, home and casualty insurance for private lines have been intensively analyzed from different perspectives.

The analyses, which can be produced flexibly, go much deeper than comparable figures of HORA, the Austrian authority for Natural Hazard Overview and Risk Assessment. Trends in the pool data of the individual sectors and in the Austrian market are identified and discussed together. Meyerthole Siems Kohlruss (MSK) also contributes comprehensive sector knowledge from the German market. In addition, there will be a comprehensive comparison of the pool data with HORA statistics as well as with other market statistics (e.g. VVO, FMA).

Highlights are, among others, the analysis of the special features and the profitability of different distribution channels. On the basis of the pool data, relevant class-specific and even risk-specific zoning was developed at postcode level. These are incorporated into the risk models of each line of business. A more targeted portfolio management is possible by joining risk models at customer level.

The statistics collected are quite comparable with those of the market-leading companies. Currently, seven insurers with a market share of approx. 18% in householders’ insurance participate in the data pool.

The sponsor of the data pool is Deutsche Rückversicherung.

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Motor Data Pool in Austria

Successful pricing through solid data basis

Increasing competition on the lookout for profitable customers is leading to increasingly complex tariffs in motor insurance in Austria as well. In order to avoid the danger of anti-selection, there is an increasing need for risk-differentiated pricing, which in turn requires a sound data basis.

After the property/accident data pool, Meyerthole Siems Kohlruss (MSK) is founding another data pool in Austria with its motor data pool combining the experience of numerous already existing data pools and the expertise from the German motor vehicle market. The data pool addresses small and medium-sized companies, which do not always have a sufficiently large calculation database and thus do not have tariff features to the extent that is common, for example, in the German motor vehicle market. The analyses and findings compiled on the basis of the data of the individual member companies cover the three usual classes of motor third party liability as well as comprehensive and partial cover insurance.

The pool is sponsored by Deutsche Rückversicherung.

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Legal Protection Data Pool

The pioneer for Germany

With a market share of currently more than 35% of the participating insurers, actuaries in this line of business can benefit from particularly detailed analyses.

Characteristics such as length of time without a claim, age of the policyholder or regional zoning models have a high potential for developing risk-adequate tariffs. Any business via comparison portals, which is particularly susceptible to special-purpose contracts, is marked and analyzed separately. The different types of claims management, telephone counselling, mediation, online counselling and lawyer networks, which are marked accordingly, are also examined and evaluated for their effectiveness.

The pool's findings can also be used to develop a differentiated individual case reserve in order to take account of claims developments such as rising amounts in dispute, reforms of the Lawyers' Compensation Act or special accumulation events at an early stage.

The pool is sponsored by Deutsche Rückversicherung.

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Functional Disability Insurance Data Pool

Innovation driver in the accident business

In the stagnating accident business, functional disability insurance has been the innovation driver in recent years.
Especially with such a novel product, a data pool is indispensable to verify the calculation assumptions at an early stage and to handle the long-term performance promises in a calculatory way. The anonymized benchmarking shows in which portfolio and claims segments the success of individual pool participants differs from others. Are the risk assessment effects reflected in the tariffs? And how much sustainability was achieved?
The development of the individual product modules accident, organ, basic abilities, long-term care and cancer can be viewed separately. In addition, the entire claims history is entered into the pool every year by the pool participants, so that changes in the amount of claims can also be recognized as early as possible.
The functional disability insurance pool is a cooperation between E+S Rückversicherung and Meyerthole Siems Kohlruss (MSK).

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Cyber Data Pool

With Big Data against digital dangers

Cyber attacks have become a frightening normality. They can affect large and small companies alike. The economic damage already amounts to about 50 billion euros in Germany. Although the types of cyber damage are very diverse, they can be recorded, classified and evaluated actuarially – and thus insured seriously.

In order to quickly achieve significant statistical significance in this regard, Meyerthole Siems Kohlruss (MSK) operates a data pool for cyber risks together with E+S Rückversicherung – the first in the German-speaking countries.

The much-cited high risk of change in this sector is adequately taken into account by involving external cyber specialists. This does not replace a well-founded examination of the actual status, however. The extent to which the loss estimates on which the tariffs are based are correct and the underwriting guidelines are appropriate for limiting the insurable risks is examined.

Individual and accumulations that offer a comparison between the insurers are defined and analyzed together with the participants.

In the future, adequate pricing and controlling of individual service components will be developed so that the products can be flexibly adapted to market conditions. Models based on technical exposure variables such as the number of electronic devices or IT security scores are also analyzed and discussed.

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Telematics Data Pool

Successful with innovation in car insurance

Since many telematics products have been available to all policyholders, telematics can no longer be seen as a niche product. There are already an estimated 750,000 telematics contracts on the German market. In the medium term, telematics products will be standard part of motor insurers’ offers.

In addition to the already complex pricing in motoring, the volume of collected and analyzed driving data poses a variety of challenges in the areas of big data, actuarial data science and data security.

In order to support insurers in all aspects of telematics, Meyerthole Siems Kohlruss (MSK) is founding Germany's first telematics data pool together with Deutsche Rückversicherung AG, thus offering insurers a complete solution – optionally including a data processing centre and a telematics app!

Your advantage: With Meyerthole Siems Kohlruss (MSK) you get full insight into the modelling of the telematics score!

Then the ever-growing claims experience can not only be used to design highly customizable, data-driven scoring models that are not modelled in isolation, but in line with the classic tariff characteristics. In addition, selection effects can also be analyzed in order to precisely quantify the extent to which the claims experience for telematics contracts differs from classic motor vehicle contracts.

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Data Management

Increase data quality

Meyerthole Siems Kohlruss (MSK) supports insurance companies in the validation of their data and the development of an analysis database, which is necessary for actuaries, be it for pricing or for the creation of portfolio and risk profiles for reinsurance. It takes a clean database to make accurate statements about the business and develop successful strategies.

Meyerthole Siems Kohlruss (MSK) uses state-of-the-art methods. In addition to standard applications such as SAS, MSK’s software ARIANE is also deployed. ARIANE is a solution for data management, validation and calculation, with which even an extensive data repository such as the one in the data pools can be processed with high performance.

In addition, it is possible to use the analyzer technology developed within the framework of the data pools and apply it to basically any data stock. The advantage of this technology, which is based on generating structured data cubes, is that evaluations can be generated quickly and easily without statistical or programming knowledge, and the output can be integrated into the familiar Excel environment as spreadsheets or charts.

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Big Data

Not just collecting data, but using it

Over 300 million data records, several hundred gigabyte of inventory and claims data, servers full of motor vehicle telematics data: It’s hard not to lose track! Due to the enormous volume of data in the data pools operated by Meyerthole Siems Kohlruss (MSK), it was essential to deal with the opportunities and challenges of big data from an early stage on.

Meyerthole Siems Kohlruss (MSK) now has many years of experience in transforming current research on various statistical topics into models that generate definite added value. For example, Meyerthole Siems Kohlruss (MSK) uses various dimension reduction methods, including Principal Component Analysis, to better evaluate vehicle driving data.

In the data pools, the data quality is increased through imputation. For example, missing data for the age of a building can be predicted from other existing characteristics using neural networks and deep learning.

In addition, it is possible to use the analyzer technology developed within the framework of the data pools and apply it to basically any data stock. The advantage of this technology, which is based on generating structured data cubes, is that evaluations can be generated quickly and easily without statistical or programming knowledge, and the output can be integrated into the familiar Excel environment as spreadsheets or charts.

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