Description: Healthcare cost is increasing at 9.3% in Asia in 2017 and health insurance cost will eventually catch up, making healthcare unaffordable to aging populations. Inefficiency in healthcare system is a contributing factor. In this session, use cases on how AI is impacting the health insurance operations on 3 core challenges. Document processing: A lot of documents are generated in the healthcare ecosystem, medical reports, diagnosis, invoices etc. Currently most of the documents are still reviewed manually and rely heavily on users’ judgement. Customer engagement: Customers are generally under distress and feel rejected during the claim process. It is due to the time and process needed to get the information they look for, the gap between the sales and claims process, and the reimbursement amount. Claim settlements: Claim adjudication is a technical process that review the disease, treatment and policy coverage. The process is specific to disease and difficult to be generalized. Thus, the process is far from consistent and invites fraud, abuse and wastage in this area.

Description: A literature review report that looks at research on how artificial intelligence is used in actuarial work.

Description: The purpose of this report is to demonstrate how artificial intelligence applications have grown since their infancy and invite the reader to consider the vast data at our disposal for the future.

Description: View the Data Challenges in Building a Facial Recognition Model and How to Mitigate Them

Description: How the speed of change is accelerating and how we could react to this as a profession.

Description: This document is a call for essays issued by the Society of Actuaries Research Institute. It seeks submissions that explore the impact of Artificial Intelligence/Large Language Models on retirement professionals and retirees. The document provides background information, research objectives, and a list of illustrative topics and questions that the essays can address. The submission guidelines, timeline, and evaluation criteria are also outlined.

Description: "The following video Dave Friesen provides a comprehensive overview of the Artificial Intelligence and Data Science learning track. It covers the role of AI in actuarial work, ethical considerations, and the challenges and limitations of AI. The document also discusses the importance of cloud ecosystems, AI tools, and automation trends, emphasizing the evolution of platforms like Python and low-code solutions. Additionally, it highlights the criticality of lifecycle management, governance, and data foundations in AI applications.

Description: This Q&A article discusses the use of machine learning to analyze unstructured data. Supriya Ramachandra, a data scientist at SIKKA, explains how her team uses natural language processing techniques, technical aspects of neural networks, LSTM modeling, and natural language processing. Machine learning allows for the classification of large amounts of unorganized data, providing actionable information for actuaries.

Description: This newsletter provides a regulation outlook for the use of AI/ML in insurance industry, interpretations of regulatory expectations and what insurers should consider when managing risks associated with the development, implementation and use of AI/ML models/tools.

Description: There is enormous interest by investors and business leaders in the potential for artificial intelligence technologies, often powered by predictive modeling and machine learning, to transform the way that companies do business.