Description: This paper described the 80-column punched card which had been adopted by the New York Life and the general procedure which was being followed in the preparation of such mortality card on new issues of policies. From Transactions of Society of Actuaries 1949, Vol. 1, No. 1.

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: This paper reviews the applications of operations research methods in the insurance industry. From Transactions of Society of Actuaries 1995, Vol. 47.

Description: Feature article about the use of approximations.

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: Opinion piece about when, how, and why actuaries need approximations.

Description: This is a general discussion about expense trends and electronics. From Transactions of Society of Actuaries 1962, Vol. 14 Pt. 2

Description: Read the April 2019 issue of CompAct, published by the Technology Section of the Society of Actuaries.

Description: Risk adjustment in government regulated health plans has been around for over two decades. Yet the strategies for promoting risk score accuracy haven't varied much during that time. Most health plans, physician groups, and vendors promote chart reviews and/or in-home assessments as the primary vehicle for collecting data not perceived to be present in the routine claims submission and adjudication stream. We know that most organizations trying to coalesce encounter data from multiple sources often fail to ensure that all relevant data is ultimately submitted to CMS or a state Medicaid agency. Crucial data, often with significant financial value, frequently gets lost in the data pipeline, somewhere between the provider's billing system and the health plan and/or its vendors. Because 837-formatted data is difficult to read, it complicates payers and vendor’s abilities to track encounter data so that every service delivered to patients is credited to the applicable risk adjustment formula. It is incumbent upon successful risk-adjusted entities to be able to track encounter data, identify processes and/or vendors which may be mangling or losing data, in order to ensure that risk scores are sufficiently accurate to be compliant with plan sponsor requirements and to ensure appropriate revenue to the health plan or provider group.