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Data science wiki

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.

Sources: https://www.investopedia.com/terms/d/data-analytics.asp
https://searchdatamanagement.techtarget.com/definition/data-analytics

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.

Sources: https://work.chron.com/data-analyst-vs-actuary-16473.html
https://study.com/articles/difference_between_actuary_data_analyst.html

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.

Sources: https://www.actuaries.org.uk/news-and-insights/news/data-visualisation-techniques-vision-actuaries

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.

Sources: https://www.actuaries.org.uk/documents/practical-application-machine-learning-within-actuarial-work

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.

Sources: https://www.cambridge.org/us/academic/subjects/statistics-probability/statistics-econometrics-finance-and-insurance/predictive-modeling-applications-actuarial-science-volume-1

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Events calendar

  • Asia Conference Webinar Series

    Webinars
    7 September 2020 - 25 September 2020

    Spaces available

    There will be a prestigious line-up of international speakers discussing the insurance and financial industry’s innovation and change in Asia.  The conference will take place throughout September via an online platform. The webinars consist of plenary speaking sessions and a series of workshop sessions including Life, GI, Data Science, Sustainability, Risk Management and Investment.

    This will be the perfect opportunity for you to discover,ask questions and be at the forefront of current and developing actuarial/financial topics and trends in Asia.

     

  • Spaces available

    This free 90 minute webinar is designed to support the IFoA CPD Co-ordinators, and others, involved in supporting our members to achieve their CPD requirements. 

    The programme will include an overview of the new CPD Scheme; specifically sharing with you key messages to support you implement and embrace the new CPD Scheme for our members within your organisation and regional community; how to arrange a reflective practice discussion; and an interactive reflective practice discussion learning exercise.  In addition, delegates will gain information about accessing, and making the most of the IFoA event Toolkits which you can make use of to run your own in-house events and events for regional communities. 

  • Spaces available

    16.00-17.00 GMT+8

    Consumer expectations are changing Insurance. The Royal Commission in Australia, Design Obligations in the UK, the insurtech ecosystem, and digital-first consumers demanding personalised solutions will all revolutionise how insurance looks like in the future.

  • Spaces available

    12.00-13.00 GMT+8

    This presenter / panel workshop hybrid will be anchored by two presentations examining the socioeconomic, medical and technological factors that will have a significant impact on mortality and our pricing over the next 20 years and beyond. It will also discuss whether significant mortality improvement will continue in Asia or whether varying experience of low improvements or deterioration. 

  • Spaces available

    12.00-13.00 GMT+8

    This presentation aims to provides an overview of the reformation of current Chinese regulatory solvency regime, how industry coping with the new normal after pandemic time and how the reformation of the regulation could help the insurance industry gets back on its feet as well as coming back to the “protection” core value for the policyholders. The presentation would include:

  • Spaces available

    16.00-17.00 (GMT+8) | 09.00-10.00 (BST)

    The basic data of China’s 2nd Critical Illness Mortality Table covers 2000+ products in Chinese market, including about 340 million insurance policies and 5.1 million claimants. Presenter will give the audience a general understanding including but not limited to the following contents:

  • Autumn Lecture 2020: Professor Elroy Dimson

    Online webinar
    14 October 2020

    Spaces available

    Many individuals and institutions have a long-term focus, and invest funds for the benefit of future generations. Their strategy should reflect their long horizon. University endowments are one of the oldest classes of institutional investor, and I will present the first study of the management of these endowments over the very long term.

  • GIRO Conference 2020 Webinar Series

    Available to watch globally in November.
    02-13 November 2020
    Spaces available

    This year's GIRO has been re-designed as a virtual conference to offer members and non-members the opportunity to get up to date content from leading experts in the general insurance field via online webinars. All sessions will be recorded and made available to purchase and re-watch post-event on the IFoA's GI Online Learning Resource area.

  • Spaces available

    Cash-flow driven investing is a game-changer for DB pension funds navigating their end-game. Suitable for sponsors who want to reduce risks on their balance sheets. And for trustees, it shifts the focus to providing greater certainty of returns, managing funding level volatility and ensuring they have enough income to pay cash-flow requirements.

  • Spaces available

    The talk will provide an understanding of the priorities and relationships between deficit reduction contributions, in the context of wider scheme funding, and different types of value outflow from the employer based on the working party’s recently published report.