The European insurance businesses ripe for M&A

Published in: Corporate strategy, UK, Rest of Europe, SFCR

Analysis of solvency and financial condition reports by Insurance Risk Data has revealed dozens of insurers with small, loss-making lines that might be ideal targets for M&A. David Walker and Christopher Cundy explain 

Rumours that Legal & General was selling its UK home insurance business to Allianz emerged at the end of April – and it got us thinking about what other businesses might be put up for sale.

The rationale for L&G to get out of home insurance was fairly clear. It represents a tiny proportion of the overall business, which is dominated by £1trn-worth of asset management activity and a thriving trade in life insurance.

The home insurance division is not a huge contributor to profits, either. In 2018, it made precisely zero pounds after suffering a double-blow of higher than normal claims from the harsh winter weather, followed by a spate of subsidence claims after a long, dry summer.

If the criteria for being up for divestment is to be a loss-making business that contributes only a small proportion of your group’s overall premiums, we wondered how one could identify other candidates for divestment at other insurers.

InsuranceERM set about examining the information contained in Insurance Risk Data, our database service that collates insurers’ financial and risk information including that from solvency and financial condition reports (SFCRs) published by insurers across the EEA under Solvency II.

Doing the analysis 

First, we decided to restrict the analysis to non-life business, although similar analysis is perfectly possible for life and health lines.

Second, we had to choose appropriate thresholds for what would make an ideal M&A target. We selected lines of business where the insurance group was making an underwriting loss (i.e. a net combined ratio of more than 100%) and where that line of business contributed to less than 5% of its total non-life premiums.

We analysed SFCR data from the 2017 year-end – the most recent and most complete year available. Insurance Risk Data has records for more than 2,400 entities from their 2017 year-end filings and is currently updating the database with 2018 filings.

About 200 European insurance groups were considered in the analysis.

Limitations 

SFCRs require insurers to categorise their non-life premiums and underwriting profitability into 12 lines of business.

(For the record, these are: medical expense; income protection; workers' compensation; motor vehicle liability; other motor; marine, aviation and transport; fire and other damage to property; general liability; credit and suretyship; legal expenses; assistance; and miscellaneous financial loss.)

These are not necessarily the categorisations that we would expect if we were thinking about analysing the insurance market. In particular, there is no distinction on whether these are personal or commercial lines.

Moreover, these categorisations may combine two or more lines of very different size and performance.

Findings

We identified almost 170 lines of business where Europe’s insurance groups are making an underwriting loss and where the premiums in that line are less than 5% of total non-life premiums. It was a good strike rate, given that some level of premiums was written in nearly 1,200 lines.

Insurers were most likely to have loss-making, minor activities in the “miscellaneous financial loss”, “assistance”, “legal expenses” and “marine, aviation and transport lines”.

Of course, there may be many good reasons why an insurer might not consider selling a particular line of business, even under these conditions.

The year (2017) may have been an outlier for losses; a more typical year might have seen a combined ratio of less than 100%.

The business itself might make an underwriting loss, but overall it may be profitable for the insurer if, for example, there are additional profit-making services or products sold alongside it.

But the analysis shows the potential for identifying M&A opportunities from the transparency provided by Solvency II regulatory returns and the analytical power of Insurance Risk Data.

  • A complete spreadsheet of our findings is available on request. Please email [email protected] for more details

Christopher Cundy, David Walker