Solvency II – A Quest for Data Quality [Part 2]

Part II – The Reinsurer


In Part I of the blog ‘A Quest for Data Quality’, we explored what Solvency II has to do with data quality, and the importance of looking upstream to your reinsurance system - Part 1In Part II of this series, let’s consider how the Reinsurer fits within the Solvency II framework.

In a nutshell, Solvency II is about modeling risk, managing risk in a well-governed way, and disclosing risk to external stakeholders. 

Reinsurance is one of the main tools available to an insurer to actively manage its risk. But it begs the question: To reinsure or not to reinsure?

In simple terms, there are two options available to an insurer with a life & health block:

  • Option 1 – retain all the risk in-house, and therefore hold sufficient reserves to meet the entire liability of that block.
  • Option 2 – cede a percentage of the risk to a 3rd party (traditionally a Reinsurer), to share the liability of the life block.

There are pros and cons of both options, but it’s fair to say most European life insurers look to cede a significant percentage of the risk to a 3rd party – 100% in many cases. Importantly, risk modeled within the Solvency II framework takes the effect of reinsurance into account. The company’s risk position is reflected net of reinsurance, and reinsurance counterparty risk is explicitly allowed for. This means that the life company can use reinsurance to optimize its solvency capital requirements and the associated cost of capital, or conversely, use its Solvency II model as a tool to decide how much reinsurance it needs.

Sounds great in theory, but how does it work in practice?

When the actuarial function calculates the solvency capital requirements for Solvency II (SII), they need data extracts from their reinsurance systems. The extracts provide the total sum at risk (net of the reinsured risk), the ceded portion of pending claims and policy reserves. These data fields have to be reported, because they represent the underlying exposure of the insurer to the reinsurers’ credit risk. Sounds tricky –  and it is!

In addition to calculating the solvency capital requirements for the ceding insurer, the data extracts are also used to provide policy and claim information to the reinsurer on a regular basis. These provide the reinsurer with reinsured sums at risk, outstanding claims and premiums paid.  

Four eyes are better than two - A few weeks ago, I grabbed a coffee with a friend who is a commercial pilot. Over the course of our coffee he explained that a plane and its pilots have a number of backup systems & processes to avoid a catastrophic failure. From a human perspective whichever of the two pilots (captain or co-pilot) not physically flying the plane for that journey, is wholly responsible for continuously validating the actions of the pilot actually flying the plane, throughout the flight.

The reinsurance data extract also comes with an external validation process, in the form of the Reinsurer. 

The Reinsurer validates its clients’ reinsurance reporting against the treaty terms and conditions on an ongoing basis. Any anomalies or queries are investigated, and quickly raised with the ceding insurer if necessary. This data quality partnership approach gives confidence to both the ceding insurer and reinsurer, to the quality of the data being used by both parties to calculate their solvency capital requirements for SII.


Having a reliable reinsurance system and solid reinsurance administration practices invariably becomes an integral part of the SII process, if the insurer is willing to take advantage of this peer-review by a (very) interested party. Reinsurers take data quality seriously. Poor data quality means that the reinsurer must cleanse the data through their own systems and run separate investigations into anomalies, both internally and externally with the client, before the data can be deemed usable. All of this costs time and money, as well as requiring additional reserves to be set up against known poor quality data, and there’s quite a few examples in Europe of insurers incurring reinsurance surcharges because of poor data quality.

On the flip-side, the insurer can get a better deal from the reinsurer if its data quality is impeccable, and it will provide comfort to the insurer when the data feeding into its SII process, passes the reinsurer peer-review test. (See here for a great article on Reinsurance Administration and the Importance of Data Integrity)

PRO TIP: Reinsurance reporting, just like SII, needs be regular, accurate and ideally of a single consistent data format. Improved SII risk modeling and accuracy in reporting are just some of the benefits to be gained from best practice reinsurance administration.  More attractive reinsurance terms and conditions will reflect the high data quality provided to your reinsurers, and the data can be used in other areas of your business such as gaining a better understanding your customers, and making your products more attractive to them.

In essence, it’s all about data quality! Part III of the blog we will discuss how all the parts come together to provide a winning proposition.

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Written by Andy Hazell (LOGiQ3) and co-authored by Kai Kaufhold (Ad Res)

Andy Hazell

Written by Andy Hazell

Head of Strategy & Business Development - EMEA

Topics: Solvency 2, Reinsurance

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