RealRate - the Independent Rating Agency.

We work purely fact-based, using cutting-edge technology. Being completely independent and fully automated we avoid the slow, biased and expensive human-driven approach. We evaluate the companies with our industry-specific expert system. Using artificial intelligence, we determine the causal relationships between the most important economic variables. For this we use structural neural networks.

The entire economic context is graphically illustrated and easy to grasp, even for non-professionals. The colored graph shows the relative strengths and weaknesses with respect to the industry average. We make all rating results publicly available on realrate.ai free of charge, as well as the scientific methods we have developed and use. This ensures transparency and comparability.

The Industry Ranking

The RealRate ranking is an evaluation of companies and not an investigation of individual products offered by these companies. We analyze the financial strength of companies, on the basis of their published annual reports. We have developed an objective and up-to-date method of analysis that takes into account the industry-specific features.

The RealRate ranking answers the following questions:

  • The financial health of the business.
  • The company’s profitability.
  • The extent of safety cushions and resilience.
  • The company’s ability to generate profits in future.

The key result is the economic equity ratio, i.e. the equity measured at fair value in relation to total assets. All German businesses that accept new customers are compared with each other in an overall industry ranking. Cross comparisons are possible due to the uniform methodology.

The Real Rate Ranking has the following advantages:

  • Holistic business model instead of a simple weighting of key figures
  • Causal analysis over the entire value chain of the business
  • Economic perspective through market-oriented revaluation of statutory balance sheet items
  • High data quality and comparability by limiting to publicly available data
  • Contemporaneity by using only the most recent annual report
  • Using the most modern methods of artificial intelligence: we use structural neural networks to check our expert system

We examine the business as a whole and do not limit ourselves to a handful of key figures. For the key balance sheet figures, we look at how these ultimately affect the financial strength. In doing so, we take into account the overall interdependencies.

A key component is the revaluation of figures from the published balance sheet, as these figures may deviate from fair market prices. This enables us to recognize the long-term, sustainable financial strength of the company.

In addition to determining the economic capital ratio, our analysis consists of a ranking of the companies active in the market. The order arises from the financial strength determined, ensuring comparability of our results and enabling benchmark analyses. The individual company is assessed in comparison with its competitors. We only use publicly available, audited and current data, in particular from the annual report. We uniformly apply our expert system to these data.

Our expert system is regularly checked using the most modern methods from the field of artificial intelligence. We check both the overall quality of the model as well as the individual causal relationships.

Objective and easily comparable financial strength ratings.

For the ranking we use the current annual reports. The balance sheets and the profit and loss statements provide the following information:

  • Available equity capital, such as equity and subordinated liabilities
  • Hidden reserves of assets
  • Amount of debts and other liabilities
  • Sources of income and profitability

The analysis focuses on economic capital, being the difference of market value of assets to market value of liability. High economic capital is a prerequisite for stable future returns. We take the data from the annual reports of the companies or other audited sources. We do not use any internal, company-specific information. This enables high quality and comparability exclusively on the basis of defined and externally audited data.

Interviews with Holger Bartel

In a series of interviews, Holger Bartel from the independent rating agency RealRate answers questions about disability insurance and the RealRate survey. All for the benefit of customers, because financial strength secures its future surplus participation:

Part 1:

What should be considered from the customer’s point of view with regard to disability insurance? What role does profit participation play?

Part 2:

Why is it important to pay attention to the financial strength of providers of disability insurance? Why are disability customers also affected by low interest rates?

Part 3:

The RealRate approach to rating insurance companies: Financial strength is brought to the fore. Using modern artificial intelligence methods, the causes of financial strength are analyzed and explained.

Scientific publications

Here you will find the scientific foundations on which the RealRate evaluation model is based. The paper „Causal Analysis with Neural Networks“ presents the artificial intelligence methods we have developed and used. The work on „Simple Solvency“ includes an early version of our expert system for the financial strength of German life insurers:

FinTech - CyberSecurity-Amsterdam

RealRate Explainable Artificial Intelligence (XAI) in Ratings
Use of Explainable Artificial Intelligence (XAI) in Ratings by RealRate. With some illustrative examples. Download Pdf

ranking report methode

RealRate Expert System Life Insurance
RealRate Expertensystem für deutsche Lebensversicherer zur Bewertung und kausalen Analyse. Download Pdf

RealRate Expertensystem BU-Beitragsstabilität
RealRate Expertensystem zur kausalen Analyse der BU-Beitragsstabilität. Download Pdf

RealRate Expert System Health Insurance
RealRate Expertensystem für deutsche Krankenversicherer zur Bewertung und kausalen Analyse. Download Pdf

RealRate Causal Analysis
With an Application to Insurance Ratings

Kausale Analyse am Beispiel des RealRate-Modells für deutsche Lebensversicherer. Download Pdf

RealRate Kausalanalyse mit neuronalen Netzen
Methodische Grundlagen für die Schätzung von kausalen Effekten mittels struktureller neuronaler Netze. Download Pdf

RealRate Simple Solvency Dokumentation
Ein einfaches Solvenzmodell für deutsche Lebensversicherer – Dokumentation. Download Pdf

RealRate Simple Solvency Präsentation
Ein einfaches Solvenzmodell für deutsche Lebensversicherer – Präsentation. Download Pdf

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