Artificial intelligence & machine learning
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Emerging Topics Community: Understanding LLMs - Tokenization
Anders Larsen and Shea Parkes are back to explore the technical aspects of LLMs. -
Emerging Topics Community: Actuaries in the Age of AI: A conversation with ChatGPT
In this captivating podcast, Joe Alaimo, CEO of ProComp, sits down with ChatGPT, one of the most advanced AI models, to delve into the future of the actuarial profession. -
AI and the Future of Actuarial Work: Shaping the Future of Insurance and Actuarial Science
In this final episode of a 3-part series, Joe Alaimo, CEO of ProComp, is joined by Deloitte AIS team members Andrew McLeod and Harrison Jones to delve further into the world of Artificial Intelligence, with a specific focus on ReportGen.AI. -
Reimagining Actuarial Reporting with AI: Introducing ReportGen.AI (Part 2 of 3)
In this second episode of a 3-part series, Joe Alaimo, CEO of ProComp, is joined by Deloitte AIS team members Andrew McLeod and Harrison Jones to delve further into the world of Artificial Intelligence, with a specific focus on ReportGen.AI. -
Unveiling AI and GPT-4: Actuarial Impacts and Insights (Part 1 of 3)
In this first episode of a 3-part series, Joe Alaimo, CEO of ProComp, is joined by Deloitte AIS team members Andrew McLeod and Harrison Jones to delve into the world of Artificial Intelligence, with a specific focus on OpenAI's GPT-4. Together, they demystify the concepts of generative AI, large language models (LLMs), and explore how GPT-4 stands out from its predecessors. -
Deep Learning in Segregated Fund Valuation: Part 2
This article is the second part of an article that appeared in April 2022 on the Emerging Topics Community webpage. It will discuss the data preparation, hyperparameter tuning and selection, and the training and testing process of the deep learning models. To reach the final conclusions, the article will continue to compare the projected cash flow results from LSTM and LSTM-Attn with those from the traditional method, and evaluate the time series generations of interest rates and equity returns by WGAN and TCN-GAN -
Anders vs. Shea, Part 4: A Champion is Crowned
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, reveal the results of the competition and share some final thoughts on the 2021 Milliman Health Practice Hackathon. -
Anders vs. Shea, Part 2: Anders’ Story
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are joined by Nick Vander Heyden to discuss the approach used by Anders’ team in the 2021 Milliman Health Practice Hackathon. -
Deep Learning in Segregated Fund Valuation: Part I
Segregated Fund is a special investment fund to provide capital appreciation with embedded insurance features. The traditional methodology to estimate the capital reserve and pricing of contracts goes to Monte-Carlo based stochastic models due to its complexity. Recent research has introduced a deep learning model, Long Short-Term Memory (LSTM), to help cash flow projection for a Segregated Fund in its whole lifetime horizon. In this paper three new deep learning models are presented: Long Short-Term Memory with attention (LSTM-ATTN) to estimate the liability reserve and pricing Segregated Fund contracts, Wasserstein Generative Adversarial Network (WGAN) for stock return forecasting and Temporal Convolutional Network on GAN (TCN-GAN) for interest rate time series generation. As an example, the cash flow projection and Economic Capital for Segregated Fund portfolio are used to compare deep learning models against traditional models in terms of accuracy and computation efficiency. -
Emerging Topics Community: Anders vs. Shea, Part 1: Setting the Stage
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are are joined by the organizers of the 2021 Milliman Health Practice Hackathon: Riley Heckel, FSA, MAAA, Austin Barrington, FSA, MAAA, and Phil Ellenberg.
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Dive into insightful content. Gain practical knowledge. Explore the latest research and key information for future use. Discover the Emerging Topics Community, an online forum that focuses on three main topic areas–Modeling, Predictive Analytics and Futurism, and Technology.