Education and events

Faculty of Actuarial Science and Insurance Research Seminars

Academic Year 2018/2019.

If you wish to attend a seminar, please book, using the link below the Seminar.  Tea and other light refreshments will be available 15 minutes before the talk begins in the milling area outside conference room.

The FASI seminars are recognised by the Institute and Faculty of Actuaries as providing 1 hour of continuous professional development (CPD) training.

If you would like to be added to the seminar electronic mailing list, please send an e-mail stating so, containing your name to

26th September - Prof Ermanno Pittacco

Professor Ermanno Pitacco
Director,  Master in Insurance & Risk Management, MIB Trieste School of Management

Heterogeneity in mortality:  A survey with an actuarial focus

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Ermanno Pitacco is full professor of Actuarial Mathematics in the Faculty of Economics, University of Trieste, and  Academic Director of the Master in Insurance and Risk Management at MIB Trieste School of Management.

He is an actuary, full member of the Istituto Italiano degli Attuari (Italy), and affiliate member of the Institute and Faculty of Actuaries (UK).

He is editor of the European Actuarial Academy series (Springer), co-editor of the “European Actuarial Journal”, and associate editor of the international journals: "Insurance: Mathematics & Economics”, “Decisions in Economics and Finance”, “Insurance Markets and Companies: Analyses and Actuarial Computations”.

He delivers lectures in continuous professional development courses and master programmes (in Italy and abroad) for both actuaries and non-actuaries, in the field of actuarial mathematics and risk management techniques.

He was awarded with the 1996 INA Prize for Actuarial Mathematics, from Accademia Nazionale dei Lincei, and the 2011 Bob Alting von Geusau Memorial Prize, together with Annamaria Olivieri, for the best paper published in the ASTIN Bulletin on an AFIR related topic.

His main fields of scientific interest are Life and health insurance mathematics and techniques, Life insurance portfolio valuations and solvency, Longevity risk, Multistate models for the insurances of the person. In these fields, he is author and coauthor of textbooks and papers published in various international journals.

10th October - Mathias Lindholm

Mathias Lindholm
Department of Mathematics, Stockholme University.

Estimation of conditional mean squared error of prediction for claims triangle reserving

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Mathias Lindholm is an associate professor and senior lecturer at the department of mathematics, div. of mathematical statistics, at Stockholm University. He is working mainly with applied probability and statistics with an interest towards insurance mathematics. Mathias also has been working at major Swedish insurance companies with risk management, investment research and internal models under Solvency II.


The aim of the talk is to present a transparent approach to account for estimation error in the analysis of prediction error of reserving methods and to provide an explicit estimator of the prediction error, in the form of an estimator of the conditional mean squared error of prediction, MSEP. The method of obtaining this estimator relies on using a certain resampling scheme, where the resampling can be carried out either conditionally or unconditionally, together with a single first order Taylor expansion.

The applicability of the approach is illustrated by applying the method to both sequential (conditional) reserving models, e.g. the distribution-free chain ladder model, as well as non-sequential models, e.g. the over-dispersed chain ladder model. In particular, we show that the suggested approach retrieves Mack's famous MSEP formula for the ultimo claim amount and we provide a simple derivation of the MSEP for the claims development result for the distribution-free chain ladder model and discuss why the resulting formula differs from already known similar results. Further, we illustrate the effect of using either conditional or unconditional resampling in the calculation of MSEP for the ultimo claim amount for the distribution-free chain ladder model. The resulting difference turns out to be small - something which can be shown to hold asymptotically for a wider class of sequential linear models to which the distribution-free chain ladder model is a special case.

If time permits we will discuss how the method applies to point process based micro models, distribution-free model selection and Bayesian analyses.

This talk is based on joint work together with Filip Lindskog and Felix Wahl.

7th November - Cormac Bryce

Cormac Bryce
Faculty of Actuarial Science & Insurance, Cass Business School.

The Dynamics of Researcher Journal Quality Perception and Ranking Divergence

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Biography: Dr Cormac Bryce is a Senior Lecturer in Insurance at Cass, and is a member of the Faculty of Actuarial Science and Insurance. His multi-method research spans from human behaviour in financial organisations to the effect of regulation, incentive structures and employee benchmarking on organisational behaviour within the aviation and financial services industry. Cormac’s research focus has been grounded in the areas of error-reporting climate and the effects of risk events on the market sentiment of financial services organisations. In his most recent industry report written on behalf of the ACCA Cormac investigated to role of leadership in boardroom strategic decision making spanning from organisations in the FTSE 100 through to the third sector and SMEs.

Abstract: We explore the drivers of researcher's perceptions around academic journal quality, and how these perceptions converge or diverge with rankings through a large-scale survey of UK business school researchers. Our survey was conducted in advance of the release of the new Academic Journal Guide (AJG) rankings list in early 2018, and resulted in 19,250 individual journal rankings. Personal and institutional demographics as well as behavioural factors are major drivers of quality perception. Of particular importance is a researcher's connection to, and investment in, the AJG system of ranking. Individual journal linkages, such as being a reviewer or having submitted to a journal, are also linked to higher perception of journal quality at odds with actual journal rank. Our research thus provides new insights into how researchers interact with journal ranking systems in light of their own perceptions of quality. We propose how the key stakeholders in journal rankings;  business schools, journal editors, ranking bodies, and the business and management community can incorporate these findings to ensure coherency between individual, school, and national assessments of research quality.

21st November - Cecile Proust-Lima

Cecile Proust-Lima 
University of Bordeaux

Dynamic modelling of multiple domains involved in Alzheimer's disease: two approaches based on multivariate latent processes.

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Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and in clinical functions such as cognitive functioning and functional disability. Until recently, these domains were mostly studied independently while they are fundamentally inter-related in the degradation process towards dementia. We propose two statistical approaches to jointly model the dynamics of the multivariate domains involved in Alzheimer's disease. In both approaches, a domain is def ined as a latent process for which measures of one or
several markers, possibly non Gaussian, are available at discrete visits in a cohort.  In the rest approach, the main objective is to understand the link between the dimensions and the diagnosis of dementia. We thus propose a joint model in which the trajectories of the latent processes are described through a multivariate linear mixed model. Rather than considering the associated time to dementia as in standard joint models,
we assume dementia diagnosis corresponds to the passing above a covariate-specif c threshold of a pathological process modelled as a combination of the domain-specific latent processes. This definition captures the clinical complexity of dementia diagnosis but also bene fits from an inference via Maximum Likelihood which does not suffer from the usual complications of joint models estimation. The model and the estimation procedure can also handle competing clinical endpoints, such as the competing death in Alzheimer's disease. The method is illustrated on a large French population-based cohort of cerebral aging in which we study the clinical manifestations (cognitive functioning, physical dependency and depressive symptoms) in link with repeated clinical diagnoses of dementia and death. 

One limit of this approach is that the link between processes is only captured by correlations. In a second approach, we aim to model the dynamic iinfluences between processes to understand the mechanisms underlying the dementia process. We defi ne for this a dynamic causal model in discrete time based on both the linear mixed model theory to capture the correlation within a dimension and equations of difference to capture the temporal inuences between dimensions. Parameters are estimated in the maximum likelihood framework enjoying a closed form for the likelihood. As causal
relationships fundamentally lie in the continuous time framework, we evaluate the impact of the time discretization in simulations. The model is then applied to the data of the Alzheimer's Disease Neuroimaging Initiative. Three longitudinal general domains (cerebral anatomy, cognitive ability and functional autonomy) are analyzed and their causal structure is contrasted between different clinical stages of Alzheimer's disease.

Seminars take place on Wednesdays 16:00 to 17:00 in Room 2005. Light refreshments available at 15:45 in seminar room. The seminars are open to everyone.

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