Tag Archives: Netherlands

Milliman to expand data science and AI capability in the Benelux

Milliman announced that it has expanded its data science and artificial intelligence (AI) capability by taking on the data science consulting team of Ortec Finance.

The new team, in combination with Milliman’s existing data science competence, will continue to support its current customers with data science consultancy projects in the Benelux to generate value from their structured and unstructured data, and to advise them on their strategic journey to become data-driven. Ortec Finance will concentrate on the use of data science in their software solutions.

“Due to digitization, companies across a wide range of industries see their data volume explode. This, in combination with rapid progress in AI technology, leads to a substantial demand for consulting services in this field. We are at the forefront of the era of AI disruption,” says Raymond van Es, who will serve as Lead Data Science & AI of Milliman in the Benelux.

Peter Franken, Milliman Principal, sees it as an important step in strengthening Milliman’s predictive analytics and modelling capabilities in the Benelux. “Having Raymond and his team joining allows us to accelerate expanding our service offering outside the financial industry as well as our technical capabilities in the area of data science and predictive analytics.”

How will COVID-19 affect Dutch mortality tables?

The COVID-19 pandemic has affected mortality in different ways. In the first half of 2020, many people died because of the illness. However, the number of deaths due to, for example, car accidents decreased as a result of the lockdown.

There continues to be a lot of uncertainty about the duration of the pandemic. How long it lasts depends on how the virus evolves, whether a working vaccine is invented, and whether the vaccine works in the long term. Because of these uncertainties, there are questions about how future mortality projections and future cash flow projections will emerge.

In this article, Milliman’s Lotte van Delft and Sarah Huijzer analyse multiple scenarios of the possible effects of COVID-19 on future mortality tables in the Netherlands.

Regulatory interest in AI: A summary of papers published in the US and the Netherlands

We are seeing an increased interest in the area of artificial intelligence (AI) from regulators recently. In this blog post, I will provide a summary of regulatory papers published in the US and the Netherlands last year. In the US, the Casualty Actuarial and Statistical Task Force (CASTF) published a paper in May 2019 aimed at identifying best practices when reviewing predictive models and analytics filed by insurers with regulators to justify rates, and providing state guidance for review of rate filings based on predictive models. In the Netherlands, the Dutch supervisors Authority for Financial Markets (AFM) and De Nederlandsche Bank (DNB) published two articles in July 2019 discussing the use of AI in the Dutch financial sector and specifically among Dutch insurers.

Regulatory review of predictive models in the US

The CASTF paper begins by defining what a best practice is and discusses whether regulators need best practices to review predictive models. It concludes that best practices will aid regulatory reviewers by raising their level of model understanding. With regard to scorecard models and the model algorithm, there is often not sufficient support for relative weight, parameter values or scores of each variable—best practices can potentially aid in fixing this problem. It notes that best practices are not intended to create standards for filings that include predictive models. Rather, best practices will assist regulators in identifying the model elements they should be looking for in a filing. This should aid the regulator in understanding why the company believes that the filed predictive model improves the company’s rating plan, making that rating plan fairer to all consumers in the marketplace.

The focus of the paper is on generalised linear models (GLMs) used to create private passenger automobile and home insurance rating plans. It is noted, however, that the knowledge needed to review predictive models and the guidance provided may be transferrable when the review involves GLMs applied to other lines of business. The guidance might also be useful when starting to review different types of predictive models.

The paper goes on to provide best practices (or “guidance”) for the regulatory review of predictive models. It advises that the regulator’s review of predictive models should:

  • Ensure that the factors developed based on the model produce rates that are not excessive, inadequate or unfairly discriminatory.
  • Thoroughly review all aspects of the model including the source data, assumptions, adjustments, variables and resulting output.
  • Evaluate how the model interacts with and improves the rating plan.
  • Enable competition and innovation to promote the growth, financial stability and efficiency of the insurance marketplace.

Additional details are provided to give guidance on how to ensure each of these points is met.

The paper identifies the information a regulator might need to review a predictive model used by an insurer to support a filed insurance rating plan. It is a lengthy list, though it is noted that it is not meant to be exhaustive. The information required is rated by level of importance to the regulator’s review. It includes information on:

  • Model input (available data sources; adjustments to data; data organisation; data in the sub-models).
  • Building the model (narratives on how the model was built; information on predictor variables; adjustments to the data; model validation and goodness-of-fit measures; modeller software; and an analysis comparing the old model and new model).
  • The filed rating plan output from the model (general impact of the model on the rating algorithm; relevance of the rating variables and their relationship to the risk of loss; comparison of model outputs to current and selected rating factors; issues with data, credibility and granularity; definitions of the rating variables; data supporting the model output; impacts on consumers; and information on how the model is translated to a rating plan).
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Analysis of Dutch insurers’ Solvency and Financial Condition Reports: Year-end 2017

In this report, Milliman consultants provide a summary of the key solvency information of the main life and non-life insurance entities in the Netherlands based on Solvency and Financial Condition Reports as per year-end 2017. It also compares the figures per year-end 2016 and 2017 of these insurance entities in the Netherlands as well as the main figures of the largest consolidated insurance groups.

Analysis of Dutch insurers’ Solvency and Financial Condition Reports

In May 2017, the first Solvency and Financial Condition Reports (SFCRs) were published for year-end 2016. This report by Milliman consultants provides a summary of key solvency information related to various life and non-life insurance entities in the Netherlands based on their SFCRs. The report also focuses on the largest consolidated insurance groups.

New Dutch Coalition Agreement addresses changes in corporate tax affecting insurers’ solvency

On 10 October 2017, the new Dutch government Rutte III of the VVD, CDA, D66 and ChristenUnie presented their Coalition Agreement. In ‘Confidence in the future, Government Agreement 2017-2021,’ the government provides an overview of the intended objectives including expected budget.

Two proposals regarding the corporate tax will affect the solvency position of insurers under Solvency II. The first proposal is related to decreasing the corporate tax rate from 25% to 21%. The percentage decreases are in the table below:

Year Corporate tax rate
2018 25.0%
2019 24.0%
2020 22.5%
2021 21.0%

Decreasing the corporate tax rate will have a decreasing effect on the level of Loss Absorbing Capacity of Deferred Tax (LAC DT), resulting in a higher Solvency Capital Requirement (SCR). In addition, the eligible own funds backing the SCR may decrease should an insurer have a Deferred Tax Asset (DTA) on the balance sheet.

The second proposal is related to mitigating the carryforward of taxable losses with future taxable profits. Currently, loss in corporate tax rules can be recovered by profit last year (carryback) and nine years into the future (carryforward). In the Coalition Agreement, the carryforward will be limited to six years. The government expects the first saving to be in 2028. The intended effective date of this rule is currently unknown. Given the first saving in 2028, our expectation is that the rule will commence between 2019 and 2022.

The second measure impacts the level of LAC DT as well. This is due to the opportunity of recovering tax receivables from the loss (SCR) as a result of the 1-in-200-year simultaneous shock with tax liabilities from future profits. Profits between the seventh and ninth years cannot be taken into account.

The same counts for the recovery of a DTA with tax liabilities from future profits. This will be more complicated.

Insurers need to realise these new corporate tax regulations when defining their capital policies and when managing stakeholder expectations on the level of the Solvency II ratio.