Actuarial Science and Insurance Education and events

Title:  Survivor Dividend in Nonfinancial Defined Contribution (NDC) plans: Theoretical Basis and its Use as a Tool to Face Longevity Risk and Improve Pension Adequacy

Abstract:

The mechanisms of nonfinancial defined contribution pension schemes (NDCs) are close to those of a fully funded defined contribution plan but under a pay-as-you-go framework. Of particular interest is how the accumulated capita of a deceased person is used, when the death occurs prior to retirement. At the moment, Sweden is the only NDC country that distributes this capital,called survivor dividend (SD) among the same cohort survivors. Without a distribution of the SD the scheme would accumulate a reserve with no clear purpose.

This research develops a model to show whether it would be justified to include the survivor dividend in the calculation of affiliate pension balances. Secondly, in the case of non-inclusion, we analyse to what extent the SD kept by NDCs can be used to cover an unexpected longevity increase. Lastly, we study whether the SD can be used to improve pension adequacy giving low-income pensioners the financial support they need.

Our findings show that the survivor dividend has a strong financial basis in the pension calculation which enables the macro contribution rate applied to be the same as the individual credited rate.  In the case of non-distribution among the cohort survivors, our results indicate that the survivor dividend can be used to set up a minimum pension that benefit 66% of the pensioners increasing the average annual pension by 9.65%.

Biography:

Prof Carmen Boado-Penas (Heriot-Watt) applies actuarial mathematics to study the sustainability of pension systems. In 2009, she was awarded a prize by the Foundation of Spanish Savings Banks for her PhD ’Instruments for improving the equity, transparency and sustainability of pay-as-you-go pension systems’. She has published more than 40 peer-reviewed papers on public pension systems in prestigious international journals and has cooperated on various projects at the Swedish Social Insurance Agency in Stockholm and at the Spanish Ministry of Labour and Immigration. In 2012, she worked as head of research on a project for the Spanish Ministry of Labour and Immigration, the aim of which was to evaluate the redistributive effects of the pension system reform in Spain. In 2020, she received the BBVA Longevia award to support pension research.

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Title:  Fairness through regularization: an approach to mitigate group disparities for multiple protected features.

Biography

Benjamin Avanzi PhD Actuary SAA CERA GAICD is Professor of Actuarial Studies at the University of Melbourne. He worked as an actuarial consultant in Switzerland and Canada, was Executive Chairman of the Board of a Swiss pension fund from 2006 to 2008, and held full-time academic positions in Australia and Canada since 2008.  He is currently on the Management Board of the Theatre Royal (Hobart, Tasmania).

Benjamin is an "Actuary SAA" with the Swiss Association of Actuaries (fully qualified actuary, equivalent to Fellow in Switzerland), an Affiliate member of the Australian Actuaries Institute, an Academic member of the Casualty Actuarial Society, as well as a member of the ASTIN section of the IAA. He is also a Chartered Enterprise Risk Actuary (CERA) and a Graduate member of the Australian Institute of Company Directors (GAICD).

Benjamin has published numerous papers in top actuarial and operations management journals. He was awarded (along with co-authors) the Hachemeister Prize twice by the Casualty Actuarial Society in 2017 and 2023, the Taylor Fry General Insurance Seminar Silver Prize in 2018, and a Highly Commended Paper Prize by the Institute and Faculty of Actuaries in 2022. He is an Editor of the ASTIN Bulletin, an Associate Editor of Insurance: Mathematics and Economics, as well as a member of the Editorial Board of the open access journal Risks.

Abstract

Ensuring fairness in machine learning models is essential for their application in various fields, particularly in decision-making processes that impact individuals. Recent advancements in machine learning have led to the development of complex models that achieve high predictive accuracy but have also revealed latent societal inequalities in decision-making processes that may have detrimental effects on certain sub-groups in society.

The concept of fairness in machine learning is complex and varies depending on the legal framework and cultural norms. Typically, fairness is mathematically encoded into a set of criteria. Group-level fairness criteria, such as demographic parity, equal opportunity, and equalized odds, emphasize the promotion of equitable treatment among various groups, particularly those identified by protected characteristics like race, gender, or age.  On the other hand, individual-level fairness ensures that individuals with similar characteristics receive similar treatment.

A significant portion of the literature on fairness centers around simple binary classification problems, where the protected feature is usually coded as binary [3]. For instance, the widely used COMPAS software in the US, designed to determine the likelihood of reoffending by inmates, has been thoroughly investigated after being found to be biased against African American individuals. While the focus is primarily on binary classification, there are also cases where regression tasks are affected by a lack of fairness, including insurance pricing [5]. In fact, the European Union Directive 2004/113/EC in conjunction with the Guidelines on the application of Council Directive 2012/C 11/01 prohibits insurance instruments from being priced differently based solely on gender differences.

Scenarios can become more complex; e.g., insurance pricing needs to be performed without considering gender differences while avoiding the use of cultural and racial origin. In such cases, it has been observed that even models constructed to be race- and gender-neutral can still discriminate within subcategories. For example, Black males may receive different treatment than White females, known as intersectional unfairness.

This paper considers data that display latent inequalities and proposes a novel regularization approach based ondistance covariance, that aims to reduce their effect on model output while adhering to a pre-specified fairness criterion, such as demographic parity. The regularizer is compatible with both regression and classification tasks and can be applied to any modeling approach that optimizes an objective. Furthermore, unlike existing methodologies, it allows for the mitigation of multiple protected features of any type, such as categorical or continuous, mitigating the impact
of differences within protected (intersectional) subgroups.

The methodology presented in this study performs competitively with some of the state-of-the-art methods that address a single protected feature. It also offers a ready-to-use statistical test that can be used to calibrate the regularization parameter and validate the satisfaction of the fairness criterion. Moreover, it can be extended to account for multiple protected features jointly, performing well in equal treatment of protected (intersectional) subgrou

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