Analysis of life insurers’ Solvency and Financial Condition Reports Year-end 2018

This report by Milliman’s Neil Christy, Stuart Reynolds, and William Smith focusses on the Solvency and Financial Condition Reports published in 2019 which refer to year-end 2018. Our analysis of the European life insurance market covers over 650 companies from 31 countries and one territory. The charts and results focus on nine of the largest European life insurance markets by the total volume of Technical Provisions.

Determining the Fair Value of insurance liabilities under IFRS 17

While the implementation date for IFRS 17 is still two years away, the deadline is a relatively short one considering the significant changes required to companies’ financial reporting results, systems, and processes.

In their first set of accounts under the new regime, companies need to show a re-stated balance sheet on an IFRS 17 basis as at the expected transition date of 1 January 2021. The calculation of the Contractual Service Margin (CSM) at the transition date is proving to be one of the most complicated part of regime’s implementation. Milliman’s Andrew Kay, Joseph Sloan, and Rik van Beers provide perspective in this briefing note which focuses on the Fair Value Approach to transition.

How can life insurers detect data outliers using the Isolation Forest algorithm?

Outlier detection is the process of identifying data points that differ drastically from so-called “normal instances” in a given data set. Outlier detection delivers critical information across many different domains in finance, such as financial reporting, fraud detection, and portfolio risk management. Recognizing anomaly patterns not only helps us detect potential errors at early stages, but also enables us to uncover potential insights on the underlying data.

This paper by Milliman’s Hyunsu Kim and Michael Leitschkis considers a technique called the Isolation Forest, which overcomes the shortcomings of classic anomaly detection algorithms. The technique has already been successfully applied across various disciplines, ranging from astrophysics to private wealth management. In their paper, the authors apply the Isolation Forest approach to the life insurance market and discuss an algorithm for an efficient automated detection on outliers in both small and large data sets.

Boards must understand differences in D&O insurance coverage

The risks exposing corporate boards, especially for public companies, to potential lawsuits continues to increase. As the premiums for directors and officers (D&O) insurance are rising for many companies, it is important to understand the nature of the coverage offered. The type of coverage purchased will affect policy limits available to protect corporate officers.

In her article “Reevaluating your D&O coverage,” Milliman consultant Joy Schwartzman highlights the difference between Side A-only coverage and Side A/B/C coverage and whether the company or the directors are the chief beneficiary of such coverage. She also explains why it’s important for a company and its board to discuss the objective of purchasing D&O insurance and how to maximize the effectiveness of the coverage purchased to meet those objectives.

What are the requirements for expert judgment under Solvency II?

Expert judgment plays a key role in the process by which firms determine technical provisions, their solvency capital requirements and the financial resources they have available to meet these requirements. In this paper, Milliman’s Eamonn Phelan looks at the central role that can be played by an Expert Judgment Register.

How can data science extract value from external data sources?

Traditionally, insurers have relied heavily on data they have collected as well as industry-specific data to inform their business decisions and strategy. However, data science techniques have become more sophisticated, allowing insurers to better understand the relationship between internal and external data sources. Predictive analytics, machine learning, data mining, and artificial intelligence are helping companies extract value from both sources.

In this article, Milliman’s Cormac Gleeson and Eamon Comerford discuss how the use of external data can complement a company’s wider data science initiatives. They also explore some of the challenges posed by working with external data.