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Bridging the gap: constructing mortality tables using data science

For life insurance and reinsurance companies, mortality tables are foundational in the pricing and valuation of products. Actuaries have historically relied on traditional methods to build these tables, using data points such as gender, smoking status, age and duration.

Simplicity, interpretability and effective variable selection with LASSO regression

In recent years, the fields of machine learning, deep learning and advanced analytics have revolutionized various domains, including mortality modelling. These cutting-edge technologies offer tangible benefits by enhancing the accuracy, interpretability and efficiency of predictive models.

The uncomfortable truths of analytics: Lessons from baseball and actuarial work

It’s been a couple of years since we tried to tie baseball to the actuarial profession, so I thought it was time to do it again.

CIA statement – Big data’s position in insurance ratemaking

Earlier this year, the Canadian Institute of Actuaries (CIA) issued a statement on the growing interest in using big data in insurance ratemaking.

Behold the API (application programming interface)

Most actuaries work with data and build models, but do most know how to facilitate the distribution of their data and models? The purpose of this article is to introduce the idea of application programming interfaces (APIs) to actuaries who may not have considered them as a way to distribute their data and models.

R vs. Python vs. EVERYTHING

Actuaries are travelling deeper into the fields of data science and machine learning, where open-source software is widely used. Two of the most popular open-source programming languages used by actuaries are R and Python. The topic of R vs. Python is already hotly debated by many professionals in the field of data science, and still discussed at length in many opinion- and fact-based articles. It’s reasonable to assume that the debate will continue if the popularity of these two programming languages is maintained.

AI ethics and regulation in insurance: Actuaries uniquely positioned for success

With the increasing use of AI within both the private and public sectors, regulations and laws to address the appropriate collection and use of data are being proposed and passed within Canada and across the world. Beyond the expected regulatory burden that will face companies using AI, there is an ethical component as well. Insurers have historically built their business on the idea of “fair discrimination,” pricing policy-holders based on variables that differentiate their risk appropriately. Insurance companies that choose to use AI algorithms are now jumping headfirst into a situation that could be potentially deemed as unethical.

Text Analytics: A Twitter case study

The increasing availability of big data and the use of predictive analytics are changing how insurers and actuaries operate. As companies face growing competitive pressures to perform, knowing how to mine and recognize the importance of data, in all forms, becomes a prime advantage.

More the same than different: Data science and its relationship with actuarial science

Data science and actuarial science are both multidisciplinary in nature; they extract insights from data and require strong understanding of the underlying business processes and domain knowledge to be successful at accomplishing the task. A deeper understanding of data science can allow actuaries to leverage data science results in their work and find more applications in emerging practice areas.

Three strategies for moving forward: A PDC update

An update from Practice Development Council Chair, George Wang, on the council’s goals and bright vision for the future of the profession and its actuaries.