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The Role of Actuaries in Public Policy Hosted by the International Association of Consulting Actuaries (IACA) and the IAA Public Policy Task Force, this webinar will examine the meaning of public policy as it relates to the actuarial profession. The webinar will feature examples of recent involvement by actuaries in the development and implementation of public policy and will seek to identify potential target audiences. Goals of the profession and its current image and desired image will be put under the spotlight. The webinar will also address the challenges for actuaries striving to make an impact in the development of public policy.

We analyze optimal consumption and pension insurance during the life time of a consumer using the life cycle model, when the consumer has recursive utility. The relationship between substitution of consumption and risk aversion is highlighted, and clarified by the introduction of this type of preferences. We illustrate how recursive utility can be used to explain the empirical consumption puzzle for aggregates. This indicates a plausible choice for the parameters of the utility function, relevant for the consumer in the life cycle model. Optimal life insurance is considered, as well as the portfolio choice problem related to optimal exposures in risky securities. A major finding is that it is optimal for the typical insurance buyer to smooth adverse shocks to the financial market, unlike what is implied by the conventional model. This has implications for what type of contracts the life and pension insurance industry should offer.

A Review of US Public Sector Pensions

Measures of Retirement Benefit Adequacy with illustrations from the SoA

Valuation of Bermudan DB Underpin Option

This webinar will feature the results from two surveys:
The IFoA Getting Better Judgment Working Party 2014 Survey
Actuarial modelling relies on two key sources of input: data and expert judgment. While methods of channelling data to meaningful outputs have historically received high level of attention from the scientific community, research into how good quality expert judgment can be obtained is less mature in actuarial research. The aim of IFoA’s Getting Better Judgment Working Party is to make useful progress in this area, providing practitioners with ways to obtain expert judgment better.


Underwriters are from Mars, Actuaries are from Findings from an Internal Prediction Survey

Human interaction with actuarial models is inevitable and requires careful study so that these models could better serve their purposes. Empirical and scientific investigations into how experts make their judgments are rarely reported in actuarial science literature. We discuss findings from a prediction survey, whose 120 respondents in a global non-life (re)insurance company were nearly evenly split between underwriters and analysts. The hypothesis that underwriters and analysts tended to give different quantitative judgments under similar levels of information and incentivisation was tested. Of the four one-step prediction problems, none gave evidence to support this hypothesis. Examination of comments revealed broad pairs of paradigms from which individuals often selected when interpreting data for prediction. Outliers situated at the most recent positions were influential. The Availability Heuristic is used to describe the strong observed differences according to geographical location. Experts are encouraged to contemplate alternatives in interpreting data for a fuller understanding of it. We highlight example research questions that could be usefully undertaken by researchers.
The talk was first given at the CAE Spring Meeting (May 2014) and then at the IFoA’s General Insurance Pricing Seminar (June 2014).

The subject of the webinar will be the presentation of the work accomplished by a team of people that met within the scope of an ASTIN working party named Individual Claim Development with Machine Learning. The main goal of the working party was to research the field of machine learning in connection with reserving as traditional actuarial work. Artificial neural networks were implemented in a cascading triangular way similar to triangular reserving methods, and the prediction results were compared with results achieved by classical reserving methods. The findings offer a better understanding of possible complexity of the nature of the claims, point out some weaknesses that traditional methods might have, and indicate a strong potential for machine learning algorithms.

Recent years have seen the emergence of a lot of research on the application of machine learning techniques in actuarial science. In particular, there has been a noticeable amount of papers regarding machine learning applied to P&C Loss Reserving. Overall, there is a growing understanding that machine learning techniques provide better prediction accuracy of the outstanding liabilities compared to traditional methods. Nevertheless, the greater accuracy is offset by difficult interpretations of results. This makes them in line of principle not suitable in an increasingly regulated world, as it is the insurance business. Our objective is to show how we can introduce elements of machine learning into the traditional actuarial reserving methods in a gradual way. We strive to achieve a balance between predictive power and interpretability by introducing step-by-step new machine learning elements, with the possibility to simply start from the legacy paid/incurred datasets underlying the loss claim triangles without introducing any cumbersome data requirement or signi cant IT investment.

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