You are here

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

Filter or search events

Start date
E.g., 25/11/2020
End date
E.g., 25/11/2020

Events calendar

  • Spaces available

    The webinar will discuss the challenges and opportunities schemes face in evaluating end game options, choosing a target state and understanding the impact this strategic decision could have on member outcomes long after the “end state” is reached. Adolfo, Kevin and Rhian bring over 60 years of experience in the industry and a variety of perspectives as scheme actuary, covenant adviser, trustee, de-risking adviser and insurer.

  • Spaces available

    Retail banking is going through a period of substantial change as it moves into the digital age. Banks have large amounts of data about their customers and about their risks. Open data application programming interface (APIs) and data science are enabling banks to use their data to offer innovative and sometimes personalised services. Data science is also adding value in risk areas such as fraud detection and cyber security. At the same time, the move to online banking is making it easier for firms including fintechs to enter banking without having to establish branch networks.

  • UK Town Hall 08:30-09:30

    Webinar
    4 December 2020

    Spaces available

    IFoA President Tan Suee Chieh would like to invite you to the Institute and Faculty of Actuaries’ (IFoA) virtual UK Town Hall 2020, hosted by Tan Suee Chieh with IFoA’s Immediate Past President, John Taylor, President Elect, Louise Pryor and IFoA Chief Executive, Stephen Mann.  

  • UK Town Hall 10:00-11:00

    Webinar
    4 December 2020

    Spaces available

    IFoA President Tan Suee Chieh would like to invite you to the Institute and Faculty of Actuaries’ (IFoA) virtual UK Town Hall 2020, hosted by Tan Suee Chieh with IFoA’s Immediate Past President, John Taylor, President Elect, Louise Pryor and IFoA Chief Executive, Stephen Mann.  

  • 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

    Patrick Kennedy, Partner at Gateley Legal and Founding Director of Entrust (a leading professional pensions trustee company), will be delivering an update on the latest legal developments during the course of 2020. With both a pensions legal perspective and over 25 years of trustee service, Patrick will seek to highlight how the letter of the law has continued to evolve against the backdrop of a difficult and challenging year

  • 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. 

  • Spaces available

    Running off the £2 trillion of UK corporate sector defined benefit liabilities in an efficient and effective fashion is the biggest challenge facing the UK pensions industry. Trustees and sponsors overseeing those schemes need to be clear on their target end-state and the associated journey plan – but too few have well articulated and robust plans.

  • Spaces available

    The actuarial skill set has much to offer the banking industry. So many of the skills that actuaries acquire during their working life translate across to the world of banking and yet banking is perceived as an alien environment to many actuaries. But is it?

  • Spaces available

    Covid-19 has required an urgent and cross-practice initiative to facilitate the extensive impact this pandemic has across all industries. IFoA members have been keen to contribute in a different way, so we developed the IFoA Covid-19 Action Taskforce [ICAT] to coordinate our effort, with a more efficient governance.

    We have over 500 volunteers and countless topics which we have amalgamated into 93 workstreams.