Tag Archives: Derek Newton

Solvency II analysis of non-life insurers

(Re)insurance undertakings across the European Union published their second set of Solvency II public reports, the Solvency and Financial Condition Reports (SFCRs) in 2018. In this report, Milliman’s Derek Newton, Marc Smillie, and Flavien Thery analyse and compare the quantitative information contained in the Quantitative Reporting Templates (QRTs) and the text within SFCRs across a number of European non-life insurers.

The analysis of the non-life market covers 870 companies from 15 countries listed, which together comprise more than £326 billion of gross written premium and nearly £475 billion of gross non-life technical provisions. The report focuses on solo entities rather than groups and includes comparison of the 2017 year-end SFCRs with the 2016 year-end SFCRs.

Solvency II analysis of United Kingdom and Gibraltar non-life insurers

In 2018, (re)insurance companies across the European Union (EU) published their second set of Solvency II public reports, the Solvency and Financial Condition Reports (SFCRs). In this report, Milliman’s Derek Newton, Flavien Thery, and Marc Smillie summarise those SFCRs as they relate to non-life insurers regulated in the United Kingdom and in Gibraltar. Their results include a comparison of the 2017 year-end SFCRs with the 2016 year-end SFCRs.

IFRS 17 Premium Allocation Approach considerations

International Financial Reporting Standard (IFRS) 17 is the biggest accounting change for insurers in many years. The new insurance contracts accounting standard also has actuarial implications. Under IFRS 17, detailed reserving outputs and granular analysis of change will be disclosed for the first time. Items such as discount rates and risk adjustment will have a direct impact on the reported profit in the accounts.

According to the International Accounting Standards Board, there is only one model, the General Model, insurance companies should use to value insurance contracts. The Premium Allocation Approach (PAA) is a simplification of this basis, which an entity may use as an approximation for measuring contracts over the remaining coverage period.

In this paper, Milliman’s Lamia Amouch, Laura Hobern, and Derek Newton present five key challenges that insurers will need to address when using PAA.

Insuring political risk

Political risk insurance can protect businesses in locations and against perils that conventional insurance policies do not cover. Issues related to increasing globalisation, political and economic instability and protectionism have made it an important line of insurance for companies seeking to safeguard their business interests abroad.

In the article “Political risk insurance: A primer,” Milliman actuaries Derek Newton and Laura Hobern discuss what types of events this insurance covers and pricing considerations. The authors also discuss the reasons its demand has increased considerably. Below is an excerpt from the article.

Political risk insurance is commercial insurance aimed to protect businesses and business ventures in locations and against perils that other conventional insurance policies would not cover. There is no standard political risk product. Instead, these policies tend to comprise cover against a variable bundle of perils that can include:

Cover can be long term or short term, depending on the event being covered. For example, cover for trade risk might last for only 30 days, but cover for a major infrastructure development might be in place for several years. In fact, contracts that are five to seven years long are normal, though very few insurers will provide cover for longer than a 10-year period. The policies, however, may not last the full term. Coverage is often one-off, especially if it is project-based.

This translates to plenty of new business in the market with a relatively high acquisition cost, but very little renewal business.

Political risk insurance is not new, but it is not yet a fully mature market. The private market started in the 1970s, and business is written primarily through the major financial centres (i.e., London, New York, Singapore and Paris). There are state players, too, such as the Overseas Private Investment Corporation (OPIC) in the United States. This government agency has been helping American business invest in emerging markets since 1971.

Increasing globalisation and the increasing willingness of commercial enterprises to operate outside their national boundaries are driving demand for political risk cover. So, too, are the rise of nationalism and political populism in addition to continuing political instability in various parts of the world. All of these factors are increasing the awareness of commercial enterprises regarding the risks they are running when operating abroad and thus increasing their appetite for cover.

Big data challenging how insurers think about business

The insurance industry has a long history of using data to make decisions around risk. However, as more and more data on risk becomes available, insurers will encounter numerous business challenges. In the Milliman Impact article “Harnessing the transformative power of big data,” consultants Neil Cantle, James Dodge, and Derek Newton offer perspective on big data and its implications for insurers’ business models, data governance, and skills moving forward.

Big data, consumers, and the FCA

Newton_DerekIn November 2015 the Financial Conduct Authority (FCA),1 a UK financial services regulator, announced that it intended to investigate the use of “big data”2 in retail general insurance in the UK. In September 2016, it announced that it was not, after all, going to pursue this investigation. Why this apparent turnaround?

The opportunities big data provides general insurers are widely acknowledged and the reason general insurers are investing heavily in this area. But with such opportunities come potential threats: big data could potentially lead to better service and outcomes for many consumers, but could it also lead to some consumers effectively being excluded from the market, or to the exploitation of consumers who are less price-sensitive than others? They are the concerns that the FCA sought to address when announcing its investigation in November 2015.

Since then, the FCA has been gathering and evaluating relevant information, mostly relating to private motor and home insurance. It has found a lot of evidence that the use of big data results in benefits to users of insurance, through products and services being better tailored for individual needs, through more focused marketing and better customer service, and through increasing feedback to consumers about the risks that they run and how to manage them effectively, most notably to those with telematics auto insurance.

While its concerns remain, the FCA concluded from this preliminary investigation that the increasing use of big data is “broadly having a positive impact on consumer outcomes, by transforming how consumers deal with retail GI firms, streamlining processes and encouraging more innovation in products and services.” As a result, it has decided that there is no immediate need either to push ahead with the full investigation that it had originally proposed or to change its regulatory framework in response to any issues raised. However, it will continue to look at big data, in particular looking for any related data protection risks and seeking to understand how big data is used in pricing.

Full details of the FCA’s views can be found in its Feedback Statement FS16/5.

1The FCA regulates the financial conduct of the financial services market within the UK and shares with the Prudential Regulation Authority the prudential regulation of the businesses within the UK financial services market.
2There is no universally accepted definition of “big data.” In the context of its investigation, the FCA considered big data very broadly, embracing data sets that are larger or more complex than have hitherto typically been used by the insurance industry, data sets derived from new sources such as social media, and the emerging technologies and techniques that are increasingly being adopted to generate, collect, and store the data sets, and then to process and analyse them.