Technology in Healthcare

The paper breaks down the contingent liability of a universal healthcare system into two components: the implicit healthcare debt and the pay-as-you-go asset. It estimates these two components in several countries (with different demographic structure) and presents international comparisons. The results show the direct correlation IHD and the age distribution per country. The paper recommends a partial prefunding mechanism in order to reduce the IHD.

Sustainability is a multidimensional concept requiring systemic thinking across environmental, economic, and social domains. While actuaries have traditionally focused on financial and environmental resilience, we will argue that human diversity—particularly neurodiversity—is equally vital. Neurodiversity recognises that cognitive variations such as autism, ADHD, and dyslexia are natural differences, offering significant potential for innovation and adaptability. In this presentation, we will examine the challenges neurodivergent individuals face, including exclusionary recruitment processes and inadequate workplace accommodations. We will propose actionable strategies to address these barriers, including skill-based hiring, individualised workplace supports, and neurodiversity-focused leadership training. Further, we will advocate for developing metrics to measure the impact of these initiatives, leveraging actuarial expertise to assess long-term value. We will draw on global case studies, such as SAP’s Autism at Work program and the International Actuarial Association’s growing emphasis on diversity, to highlight best practices. By integrating neuro-inclusivity into actuarial workplaces, professional societies, and global standards, we will demonstrate how actuaries can drive systemic change. Ultimately, we aim to show that neuro-inclusivity is not just an ethical imperative but a strategic opportunity. By embedding these principles into the profession, actuaries will enhance innovation, strengthen decision-making, and position themselves as leaders in shaping a more inclusive and sustainable future.

With the outbreak of Covid-19 pandemic, the Brazilian supplementary healthcare sector became a conducive environment for using complex data analysis and modeling tools. In this study, we apply different Machine and Deep Learning techniques (SVM, XGBoost and RNN) to predict healthcare expenses and evaluate if these techniques would present better performance in comparison to traditional ones, such as time series and regressions. Prediction scenarios were generated upon expense official databases between 2015-2022, considering two panoramas: (i) real, and; (ii) counterfactual, in which we assume the non-existence of the pandemic data for 2020. Using RMSE as the performance indicator, we find out that XGBoost model presented the best performance for the real panorama, with better fit in 32.2% of the scenarios. For the counterfactual panorama, we observe that RNN and SVM models obtained better fit in 22.3% of the cases. It is noteworthy that, until now, no studies were identified that address the use of predictive Machine and Deep Learning models into the Brazilian healthcare expenses. We also expect that this study offers insights for decisions made by the several players in this sector, such as operators and regulators, especially when it comes to pricing and development of healthcare products.

What are the levers of attractiveness of long-term care insurance that we need to act on? In France, the creation of a 5th branch of Social Security dedicated to autonomy is a significant step forward. However public finances will not be able to assume the costs linked to the loss of autonomy of all our fellow citizens and families will not have all the financial resources to absorb the remaining expenses of their elders. These issues undeniably argue for the use of insurance solutions to support the public authorities. It is therefore essential that we, as actuaries, continue to work on making our long-term care products more desirable and durable. This workshop will be an opportunity to present an overview of the long-term care insurance market, highlighting in particular the issues, practices, market projects and prospects specific to this risk.