To what extent do actuaries already use data science tools in the healthcare sector? And from your perspective, what are the benefits of establishing transferable skills between data science and actuarial science?
In healthcare, one of the most impressive uses of data science I’ve come across is hospitals using analytics to forecast the number of patients likely to arrive at their doors in the coming week. Hospital managers use these predictions to adjust staffing and other resources to ensure anticipated patient demand is met. This forecasting is particularly useful in times of peak demand, such as ensuring hospitals have the capacity to cope in a severe winter flu outbreak.
But these models are only being used in a few isolated hospitals. As actuaries, we’re in a good position to draw on our understanding of these data science techniques to help scale-up and encourage more widespread adoption of predictive healthcare tools. We can also use our strong skills of communicating technical information to non-technical stakeholders. This supports healthcare decision-makers to understand, and react appropriately, to these dynamic healthcare forecasts.
What kind of framework must be in place for data to be readily exchangeable across health system stakeholders? And what potential exists to scale-up opportunities in using this data for predictive healthcare demand modelling?
Having common data storage and programming languages is vital if we’re to succeed in embedding predictive healthcare demand modelling right across the health system. Initiatives, such as the FHIR (Fast Healthcare Interoperability Resources) standard, are a great first step in moving towards a system where data can be exchanged across the health service. It’s important to have robust data privacy and data governance frameworks that empower individuals to engage with their health and lifestyle data.
I envisage a personal health record system that places individuals at the centre of their data. The personal health record would allow each person to take ownership of their data and decide whether and when to share this data with their medical team. Individuals could also give consent for the de-identification of their data to be fed into medical research and healthcare resource management activities.
As adoption of AI-based data science tools in healthcare increases, how important is it to ensure that algorithms can be embedded into society in a fair, ethical way, to identify and mitigate possible algorithmic biases? And what can actuaries contribute to public debate around this topic?
The adoption of AI-based data science tools is on the rise throughout pretty much all aspects of our daily lives. With algorithmic decision-making becoming so pervasive, it’s important to ensure we embed this technology into society in a fair and ethical way. Actuaries have much to offer in this area. From a technical standpoint, we can help to identify and mitigate biases that are present in training datasets being used by the models. We also have a lot to contribute when it comes to developing these algorithms. Many such algorithms are designed to maximise resource efficiency. But we should be going further to consider fairness as part of the resource allocation process.
In a field with such a rapid pace of development, ethical adoption of new data science techniques is critical. Particularly as regulation can often take time to catch-up to technological change. As actuaries, we’re well-situated to engage directly with the public and other stakeholders, to help them to understand how their data is being used by these algorithms. We can help to ensure that algorithmic decision-making is transparent and explainable. This will give the public greater opportunity to voice their opinions on what they believe is a fair and ethical way for this pervasive technology to be embedded into our lives.
What career opportunities for actuaries to work alongside data science specialists in the healthcare sector do you see – now and going forward?
The knowledge and expertise that data science experts bring when analysing large volumes of specialised medical data helps to drive developments in healthcare analytics. As actuaries, we are likely to remain highly reliant on data science experts for these deep insights into specialised datasets. Also as actuaries, we are well-placed to work closely with data science specialists by combining these healthcare analytics insights with our strong understanding of business, finance and risk management.
This could lead to growing career opportunities for actuaries to work alongside data science experts to build models that optimise financial and medical resources. I’d like to see actuaries and data science specialists collaborating with a wide range of other stakeholders such as healthcare professionals and medical researchers to help develop more effective, personalised treatment pathways and thereby improve health outcomes for society.
With specific reference to insurance, how do you believe data science techniques will prove of the greatest value to actuaries working in this field of expertise?
We’ve only just begun to scratch the surface in terms of thinking about how to embed the wide array of emerging data science techniques into our actuarial work. There’s tremendous potential to drive value creation by applying these tools to improve our understanding of how risks interact and to optimise insurance portfolios. It’s interesting to consider this question in the context of broader technological change. We’re increasingly seeing consumers adopting wearable tech, mobile apps and other connected devices into their daily lives. This technology is helping people to engage with their health more often and in new ways. With strong data privacy and data governance frameworks in place, there may be the opportunity for actuaries to use data science techniques to gain a better understanding of customers’ key insurance needs.
Using this deeper understanding of the customer, actuaries could champion the development of new insurance products that are more dynamic in their response to individuals’ health and lifestyle needs. With a growing availability of data and with technological developments continuing to raise customer expectations, actuaries will increasingly adopt more advanced data science techniques. I’d like to see us collaborating with a wide range of experts to embed data science into the insurance industry in a way that enhances business value and drives improvements for customers and society.
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Delivered by the IFRS 17 Contractual Service Margin working party.
The Certified Actuarial Analyst (CAA) qualification has rapidly established itself as adding real value, to insurers and consultancies, and to the clients of consultancies, around the World. CAAs work alongside actuaries and actuarial students, as well as other financial services professionals, in an increasingly broad range of roles and fields.
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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.
This year's Life Conference 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 life insurance field via online webinars. All sessions will be recorded and made available to purchase and re-watch post-event on the IFoA's website.
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