Description: Article from The Actuary, June 1971 – volume 5 - Issue 6 is a recap of the LOMA Systems Forum held in New York, March 15-17, 1971.

Description: The cloud inspires awe and fear in equal parts these days. There is a need to learn more, but there’s much information to learn and it may be difficult to know what to learn next. In this session, we’ll carve off a small corner of the cloud and make it your own. Setting up a Data Science Virtual Machine on Digital Ocean will be demonstrated. It’s the fastest way to get a data science development environment setup for your own use. Many of the topics and ideas covered will apply to almost all other cloud offerings. What use cases Digital Ocean is good for, and what use cases you should look elsewhere for, will also be discussed.

Description: Models are a staple of an insurance company's operations. However, models that are not appropriately governed may lead to underpriced products, under-funded reserves, unfairly treated customers, and potentially catastrophic operational, legal, and reputational risks. Model Risk Management (MRM), an essential component of an insurer's ERM function, needs to keep pace with the rapidly evolving model ecosystem to limit model risk and produce results that management can trust when employing company strategy. With the adoption of ASOP #56, emerging technologies, and regulatory changes such as PBR, LDTI and IFRS 17, it is increasingly important for MRM frameworks and processes to adapt, upgrade and evolve. Discuss insights on how to embed ASOP 56 into Model Risk Management (MRM) and assess the design, development, modifications, and validation of actuarial models, the advancement of analytics and technology that empower insurers for process optimization and automation, as well as the the key components, stakeholders, best practices, and strategic benefits of a comprehensive MRM framework. Go over use cases on enhancing MRM in the context of ASOP 56, technology, and model governance. Explore how automation and optimization of the MRM process will allow companies to realize the strategic benefits of MRM, ensure successful adoption of regulation changes, and the pros and cons of emerging technology on MRM. By attending the session, you will be able to: - Describe how ASOP 56 can help improve MRM processes. - Describe the key components, stakeholders, best practices, and strategic benefits of a comprehensive MRM framework. - Identify where and how to use technology to automate and optimize the MRM process. - Evaluate the impacts of emerging technology on MRM.

Description: A new health insurance pricing model takes external market prices into account, as well as regulatory rate restrictions and other traditional pricing factors. Rather than being assumed at the outset, lapse rates emerge from the model, along with assessment spiral & loss ratio projections. Under the model optimal pricing strategy depends on whether the class being rated has a healthier than average mix of risks.

Description: This is a discussion of the paper, New Possibilities in Graduation by Kingsland Camp. From Transactions of Society of Actuaries 1955, Vol. 7, No. 19.

Description: From a session at a meeting of the Society of Actuaries held in San Diego, CA, June 22-23, 2000 The panel discussion covered: · The basic concepts underlying fuzzy logic · Current applications to risk classification and pricing · Possible future uses of fuzzy logic · The basic concepts underlying neural networks · Current applications of neural networks in life insurance · Possible future uses of neural networks

Description: From a session at the 2000 Valuation Actuary Symposium held in Washington DC, September 14-15, 2000 Discussion of financial modeling considerations including: • Model platforms • Model structure • Static and dynamic model validation techniques • Data integrity

Description: This is a discussion about computer models and simulation tools for life insurance companies. From Transactions of Society of Actuaries 1969, Vol. 21, No. 59AB

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.