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

Description: This second session in the series examines responses to the changing workplace landscape from outside the insurance industry. Using a question-and-answer approach with our panelists, this session focuses on practical approaches to identifying new opportunities, and ways in which traditional products and sales channels are changing to meet emerging needs. Compared with other vertical financial services, the life insurance industry has a very low level of 'touch' with its customers. Communication with customers is often limited to the annual mailing of premium invoices. Wellness platforms offer life insurers a means to increase customer engagement, building stronger brand affinity and improving persistency. Life insurers can also leverage customer data collected through the wellness platform to streamline the underwriting process for cross-sales, increasing volume and efficiency.

Description: Business success in the future depends on assumptions we make today. However, we operate in a dynamic environment and cannot know with certainty what lies ahead. Addressing the challenge requires a synergy between data science, expert judgement, and decision making to spot changing drivers of key risks and respond with appropriate business action. We will be presenting development of such an "ecosystem" technically enabled by Natural Language Processing to monitor and detect signals from news streams. NLP is of increasing importance for insurance companies. Additional application examples are available in session 23.

Description: Actuaries have a long history with stochastic modeling. Nested stochastic modeling is often used when the modeling components under each economic scenario are themselves determined by stochastic scenarios in the future. In this paper three methods have been suggested that can be added on top of existing enhancement methods to improve NSM efficiency.

Description: Peer-to-peer (P2P) insurance is one such business model where individuals or economic agents join together and pool their resources for mutual aid. The aim of this project is to showcase the development of a P2P insurance model and analyze it from both technological and product perspectives.

Description: On August 29, 2022, the SOA Research Institute assembled an industry expert panel to discuss current issues in actuarial technology. This was the third time such a panel was assembled, and most panelists had participated in the previous discussions. Each participant volunteering for being part of the discussion was selected because of their management-level responsibilities for the application of technology in an actuarial context. The group was diverse in terms of employment, including company actuaries from life, health, and property/casualty backgrounds, as well as consultants from various kinds of firms. This panel included participants from outside the U.S. as well.

Description: Decentralized insurance alternatives is a rapidly expanding sector of Decentralized Finance (DeFi), an emerging and growing financial ecosystem powered by blockchain technologies. The purpose of this report is to review and discuss the business models and operating designs of decentralized insurance alternatives, which may have significant implications for traditional insurance businesses, as well as the actuarial profession.

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

Description: The SOA’s Artificial Intelligence Working Group explored the role of AI in actuarial practice, focusing on risk management, regulatory compliance, and ethical considerations. The discussion highlighted the importance of the NIST AI Risk Management Framework and Generative AI Profile in ensuring responsible AI development and deployment in insurance and healthcare.

Description: This abstract describes a paper that studies statistical models for records of data breaches due to cyber security incidents since 2005 collected by Privacy Rights Clearinghouse.