This is the first iteration of an actuaries’ data science wiki. The aim is to expand, refine and develop this wiki to encompass a knowledge-base that scopes the key terms and essential definitions of disciplines associated with data science practice, that have particular resonance for actuarial professionals.
Data analytics Data analytics is the discipline of analysing data sets to make conclusions about that information. Data analytics techniques can reveal trends and metrics that would otherwise be undiscoverable in massed information. This information can then be used to optimise processes to increase the overall efficiency of business or system operations.
Data analytics is a broad term that encompasses diverse types of data analysis. Any type of information can be subjected to data analytics techniques to gain insight that can be used to achieve improvements. Some of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work through raw data for subsequent human analysis.
Data analytics methodologies include exploratory data analysis (aims to find patterns and relationships in data), and confirmatory data analysis (applies statistical techniques to determine whether hypotheses about a data set are true or false). EDA is comparable to ‘detective work’, while CDA is comparable to the ‘work of a judge or jury during a court trial’ (John W. Tukey, Exploratory Data Analysis, Pearson, 1977).
Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive – it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes, and points of view.
Data analysis, data analysts Data analysts and actuaries share similarities. They have comparable skill sets, and use mathematics, statistical techniques, and computer knowledge to compile and analyse data, and to report conclusions for business decision-making. The two disciplines differ in the scope of their work and employment settings.
For instance, data analysts work in a broad variety of vertical sectors and industries with multiple types of data. They apply mathematical and statistical techniques to extract, analyse and summarise data. They use spreadsheet and statistical software, work with relational databases, and prepare charts and reports of their findings. Their work transforms large, complicated data sets into usable insights that inform organisational leadership decisions and policies.
Data analysts review information and use the data to help develop recommendations. They do not specifically focus on risks, but may help determine appropriate business or financial decisions that will benefit a company.
Data visualisation The main goal of data visualisation is to communicate information clearly and effectively through graphical means. and by maintaining a library of data visualisation techniques. The IFoA Data Visualisation Working Party was established in 2017. Its vision is that data visualisation for actuaries should represent:
- An understanding of which visualisations work well for different purposes.
- Domain-specific examples of helpful practice.
- An understanding of how to produce the visualisations, including tools and techniques.
- An understanding of the principles of developing and improving data visualisations.
- Awareness of caveats that should be associated with data visualisations.
Machine Learning Machine Learning is a discipline that uses study of algorithms and statistical models, as used by computer systems, to perform specific tasks without use of explicit instructions: Machine Learning instead relies on patterns and inference. It is generally regarded as a subset of Artificial Intelligence.
The question of what Machine Learning could bring to actuarial work is something of a contentious issue within the insurance sector. Some have speculated on Machine Learning’s capacity to replace manual actuarial work, and therefore reduce insurers’ requirement for human actuaries. Other argue that data science-savvy actuaries could turn knowledge of Machine Learning into a useful asset in their skills offering.
Predictive modelling Predictive modelling involves the use of data to forecast events. It relies on the capture of relationships between explanatory variables and the predicted variables from past occurrences, and the exploitation of this to predict future outcomes. The forecasting of future financial events is a core actuarial skill. Actuaries routinely apply predictive-modelling techniques in insurance and other risk-management applications.
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Wicked Problems, Clumsy Solutions and Leading Change
Dr Catherine Donnelly will present the basics of the structures for pooling longevity risks and summarise recent research results in this area in addition to outlinging future research around this topic. This is work under a research programme funded by the IFoA's Actuarial Research Centre, called 'Minimizing longevity and investment risk while optimising future pension plans'.
Climate-Related Risk - This free to view webinar on Climate-Related Risk is the first in a series focusing on some of the ‘Hotspots’ identified in the JFAR Risk Perspective bringing the Risk Perspective to life with practical illustrations and insights from subject experts from the IFoA and other Regulators
Recent decades have seen institutions, such as employers and financial services, give people more choice and flexibility, but these freedoms have come with more responsibilities. Individuals are now responsible for managing more of their own financial risks, from ensuring they put enough money into their pension to securing affordable protection to be financially resilient.
Join us for this brand new IFoA webinar weries comprising of a fortnight of webinars, panel sessions and a hackathon, that showcase the range of ways in which the actuarial profession has added value, in the public interest, to the understanding and management of the current and future pandemics through insight and learning.
This event is now temporarily closed on Monday 26 April, but the session will be repeated on Tuesday 27 April, 09.00-10.30. Please click here to register your place.
Actuaries have a lot to offer biodiversity management over the next decade as the world develops more depth to its response to this global challenge. This sessional offers an opportunity to learn about this emergent risk, to contribute to our thinking as a profession and help us develop the next steps forward.
IFoA Immediate Past President John Taylor would like to invite you to the Institute and Faculty of Actuaries’ (IFoA) virtual Europe Town Hall, hosted by John Taylor with IFoA Council Members Alan Rae, Jennifer Hartley, Maribel Vasquez Flores and IFoA Chief Executive, Stephen Mann.
Mis-estimation risk is a key element of demographic risk, and past work has focused on mis-estimation risk on a run-off basis. However, this does not meet the requirements of regulatory regimes like Solvency II, which demands that capital requirements are set through the prism of a finite horizon like one year. This paper presents a value-at-risk approach to mis-estimation risk suitable for Solvency II work
This year's Finance and Investment Virtual Conference takes on the timely theme of ‘resilience’, something we have all learnt a lot more about in the last year! Our diverse range of talks will explore the theme of resilience in a variety of ways including in building robust investment portfolios, in the incorporation of ESG factors, in govern
This talk will explore the potential benefits that wearable tech can bring to health & protection insurers and their customers. The traditional approach of integrating wearables into insurance has largely focused on measuring steps and using rewards-based incentive programs to encourage more activity.
Join us for this talk with Professor Sir Adrian Smith as part of the 'Dr Patrick Poon Presidential Speaker Series'. Professor Smith joined The Alan Turing Institute as Institute Director and Chief Executive in September 2018. In November 2020, he became President of the Royal Society, in addition to his leadership of the Turing. He is also a member of the government's AI Council, which helps boost AI growth in the UK and promote its adoption and ethical use in businesses and organisations across the country. He received a knighthood in the 2011 New Year Honours list.
We continue to live in a world of global uncertainty. Survival depends on our ability to simultaneously navigate through the diverse root-causes, ranging from: the consequences of Climate Change; on-going financial consequences of the COVID pandemic; or self-imposed changes in regulatory requirements and accounting standards.
Welcome to the programme for our 2nd Virtual Pensions Conference. This year's conference features 11 webinars offering members and non-members the opportunity to get up to date content from leading experts in the pension industry. There will also be opportunity to ask questions and contribute to the discussion.