Description: Article about Prompt Engineering Tips.

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: PartnerRe Life and Health is rapidly growing its presence in global markets. This growth means that our medical underwriting team has to handle 25 percent more cases every year. To manage these additional cases without increasing headcount at the same pace, the team has to operate more and more efficiently. In a market where response time is a key differentiator, we improve efficiency by using machine learning to triage and prioritize the underwriting team's case work. In this talk, we describe the model pipeline end to end. That is, we cover the entire implementation chain from accessing data to the eventual evaluation of the added value. In particular, we discuss often neglected topics such as model risk mitigation, technical deployment of the model and integration into business processes.

Description: Short Form Professional Development Videos - Explaining AI Black Box Part 2 Recording

Description: Neural Networks are no longer limited to classification and regression solutions to numeric problems. CNNs add the ability to classify images, and GANs can create new images. Learn how these capabilities can provide competitive advantages to early adopters, how they work, and how to implement them.

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.

Description: The barrier between humans and machines continues to blur, but curiously, this may have been prediction thousands of years ago in ancient myths. This article looks to how some of the early legends are becoming manifest in modern technology, particularly in the area of elder care. From automation, to humanoid assistants, to mind clones will the age of technology bring a new utopia?

Description: The article urges actuaries to see AI not just as a tool but as a strategic partner, drawing parallels to past transformative technologies like the printing press. Using NAIC survey data, it shows AI adoption is surging in health and auto insurance, lagging in life. It outlines AI’s potential to boost productivity, improve data quality, and enhance contract analysis, while warning actuaries to master AI before others use it to evaluate their work. Ethical standards demand transparency and fairness, making responsible AI use part of professional duty. Historical examples—like James C.H. Anderson’s 1970s cash flow testing breakthroughs—highlight how visionary actuaries can redefine the profession. The article applies Harvard professor Jeff Bussgang’s “hair-on-fire” innovation lens, suggesting AI-powered, urgency-driven insurance products, akin to Y2K coverage, could revolutionize offerings. It calls on actuaries to address protection gaps, discrimination, and industry opacity through AI-enabled fairness. The message: courage, creativity, and proactive adoption will let actuaries shape—not just survive—the AI-driven future. The closing reflects on the author’s tenure with the SOA Reinsurance Newsletter, framing this as a final, forward-looking call to action for actuaries ready to lead in a transformed insurance landscape.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: Predictive modeling is a powerful toolset for assumption setting (mortality, lapse, etc.). The multivariate approach has its advantages in handling high-dimensional data, address correlations, and statistical significance. However, building insurance assumptions is far more than a simple modeling practice. One must take care to address how the model is applied and its business implications. This session will share the learnings from multiple projects.