Description: Ignite-style presentations feature presenters with 20 slides, automatically advancing every 15 seconds. The result is a fast and fun presentation which lasts just five minutes. The first portion of this session will feature insurtech innovators doing Ignite-style presentations on various insurtech topics. Then, stick around for the second portion when all presenters will take part in a moderated, talk show-style discussion. You'll get a chance to ask questions and dig deeper into the topics you just heard about. Find out how you can be part of the insurtech revolution. Be prepared for a fast-paced, thought-provoking session that you'll remember long after it ends!

Description: Artificial Intelligence (AI) is expected to change the insurance industry more in the next 10 years than it has changed in the past 100 years. Rapid advancements in computing power and the exponential growth of Big Data are creating more opportunities to incorporate AI techniques and methodologies into actuarial work. This session is for the practicing actuary who would like to learn more about AI and how to use it to analyze insurance data. At the conclusion of the session, attendees will be able to understand the fundamentals of AI, to build an AI model and to identify AI opportunities in their work.

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

Description: The article explores how integrating AI into insurer apps can address critical challenges in healthcare, such as patient no-shows, medication adherence, and cost management. These non-disruptive approaches aim to increase the effectiveness of patient interventions and improve medication adherence.

Description: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.

Description: A look at current AI usage and future use cases.

Description: It was an honor, for Dr. Tianyang Wang, Co-Eeditor of Expanding Horizons, to have this Fireside Chat had a great conversation with Dr. Guillen Guillen Estany in January 2023, and invited her for this featured Fireside Chat interview which resulted in a great conversation.

Description: This report outlines a framework for interpretable machine learning algorithms for detecting fraud in health insurance. It discusses the challenges of using black-box machine learning models in the insurance industry and proposes various interpretable methods to enhance transparency and understanding.

Description: Big data, predictive analytics, machine learning and AI will likely transform the insurance market and the actuarial profession. The actuarial profession is seeing changes many markets, including health and property and casualty. This article describes these trends and provides an introduction into what companies and the Society of Actuaries (SOA) are doing to adapt and capitalize on these trends.

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.