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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The purpose of this research paper is to explore enterprise risk management lessons which can be learnt from the Covid-19 pandemic in preparation for potential future pandemics as well as other “gray rhino” or “black swan” events. This paper is not intended to be an all-encompassing solution to the issues presented by Covid-19; rather, the content has been provided to help drive discussions regarding how risk management processes may need to evolve in line with the dynamic nature of the underlying risks that they sometimes need to capture.
This webinar will discuss good exam technique, including various approaches candidates can take in managing their time completing their exams in the online format.
This session is for new candidates and existing candidates where we will be discussing the practical steps you need to take leading up your exam and on the day. We will be discussing how to testing the online exam platform, downloading and uploading your paper and key information from the Exam Handbook.
The exam webinar is for candidates, new to IFoA exams and returning candidates, sitting in the September 2022 exam session.
The role of Non-Executive Directors has become increasingly challenging and critical over the past few years.
Big picture thinking, Governance knowledge, Independent mindset, Ambassador potential and Energy and commitment: these are the essential skills sought in a successful NED, according to the Chartered Governance Institute (UK & Ireland).
In parallel, Environmental, Social and Governance (ESG) criteria are increasingly key and used by investors to measure the sustainability and ethical impact of investing in an organisation.
This webinar will cover:
• Some background on the risks of misselling in an ESG context, including the DWS case
• Achieving positive impact is a strong antidote to the risks of greenwashing or ESG misselling, however this risks having a tension with fiduciary responsibilities
• This tension can be resolved with a concept called Universal Ownership
• Under Universal ownership, investors have an appetite to make a loss in order to achieve positive impact, and yet still have no compromise on their fiduciary responsibilities
In the UK, the idea of collective defined contribution (CDC) pension schemes is gaining more attention with the launch of the Royal Mail CDC scheme, the first of its kind in the UK. Our recent research on CDC plans investigates the sources of the putative benefits of CDC schemes: the smoothing of pensions for members. Using an attribution analysis to burrow into the scheme design, the reason for the smoothing of members' pensions is explained and understood.
The IFoA's Infrastructure Working Party, led by Chris Lewin, will present its new introductory guide to infrastructure investment, which will be published on the IFoA web-site prior to the webinar. Those readers whose institutions have already taken the plunge into infrastructure will know that it is a highly complex and diverse field of activity. This guide does not explore all the matters which investors take into account, but it does discuss many of the more important points, including the risks and past returns, benchmarking, and ESG and SDG considerations. Attendees will be invi
Social care reform has long been on the to-do list for successive governments over the last two decades. In February, the government’s proposed reforms to adult social care [including cap on care costs] was published. Against this backdrop of funding promise and rising National Insurance taxation, in this session we will debate the resilience of these new proposals, the impact of future demand for care services and what role for the insurance industry and the important role it has played in long-term care funding in other countries where public-private partnership works.
Health contributes to happiness at the personal, family, community and societal level. Health, importantly underpins all our economic security. This talk will explore the drivers of our health, the measurement of health and the steps we can take to improve health – most of which lie outside the NHS.
We are delighted to announce the return of GIRO as an in-person conference, giving you an opportunity to connect with actuaries in your practice area. Join leading experts to discuss key issues, emerging ideas, and new research across the General Insurance sector.
Life Conference returns as an in-person conference in 2022, giving you an opportunity to connect with your peers and fellow actuaries in your sector, in person. You will also hear leading experts discuss key issues, emerging ideas, and new research across the Life insurance sector.
Mortality and morbidity risk varies by variables such as age, sex and smoking. In traditional actuarial experience analysis, these variables, and certain combinations thereof can be explored. However, with the wealth of data now available it is becoming increasingly challenging to identify the key drivers of experience and account for the interaction between different variables. A univariate approach often compares apples and pears, for example males are more likely to smoke and have larger policies than females. Likewise, variable interactions are missed unless specifically included.