The actuarial profession is going through a transformation in the age of big data and analytics. Actuaries were pioneers in life insurance, constantly evolving over time and branching into general insurance, finance and more recently into ERM (which was referred to as the fourth kind of actuary by Paul Embrechts in 2005)
The actuarial profession is going through a transformation in the age of big data and analytics. Actuaries were pioneers in life insurance, constantly evolving over time and branching into general insurance, finance and more recently into ERM (which was referred to as the fourth kind of actuary by Paul Embrechts in 2005). We believe now is the time for actuaries of the fifth kind - a profession that is recognised as a leader in data and analytics across industries.
The Singapore Actuarial Society Data Analytics Committee has recently produced a case study in which predictive analytics methods are applied to an historical retail banking dataset, in order to yield insights for marketing of a term deposit investment product.
The primary focus of the paper is to illustrate the typical framework and concepts used by data scientists within the machine-learning paradigm. The data set is an often-used one, and the case study aims to demystify the machine learning approach to fledgling actuaries coming to predictive analytics for the first time. Links to the source code used to create the results in the paper are also provided. The full paper may be downloaded from: actuaries.org.sg
The Data Analytics Committee is the Singapore Actuarial Society's initiative to explore the future of big data, analytics and unstructured data in Asia, and what actuaries need to do to have the skill-sets that will be in demand for such work.
The committee is made up of actuaries and data scientists based across Asia and globally from a diverse range of industries. For enquiries, please get in touch with Frederic Boulliung: [email protected]