Description: The management of the investments of an insurance enterprise has undergone a considerable evolution since the pioneering work of Redington in the mid 20th century. Best practices have evolved to incorporate ever more complex insurance liabilities, expanding investment asset classes and global market opportunities. Strategic asset allocation (SAA), liability driven investing, and other robust optimization approaches form the core of the professional management of insurance company investments.

Description: With many complex regulatory changes on the horizon as well as pressures to ensure model accuracy from auditors, regulatory requirements and new actuarial standards of practice, the importance of robust model validation is coming to the forefront of actuaries’ minds. What was previously a day-two task for many companies is becoming just as important as model development! In this session, you will hear from those that have been deeply involved in building model validation frameworks and tools in responses to regulatory changes GAAP Long Duration Targeted Improvements (“LDTI”) and Principles Based Reserves (“PBR”).

Description: This 1979 session is about Life Company performance measures and pricing. Topics included: 1. What are appropriate performance criteria? a. In terms of financial results b. In terms of growth 2. How are goals established? a. For Mutual companies b. For Stock companies 3. What measurement tools do we use? a. Comparison of actual to expected results b. Compound growth rates c. Rules of thumb for various performance ratios d. Analysis of source of earnings 4. How are results used in pricing? a. Pricing consistent with goals are goals consistent with projections? b. Problems relating to allocation by line

Description: This session at the SOA 1990 Hartford Spring Meeting is about AIDS from the reinsurer's point of view focusing on underwriting and the reinsurance market.

Description: This discussion at the SOA 1990 San Francisco Spring Meeting covers living benefit riders including benefit designs, regulatory activity, tax status, pricing considerations and reserves.

Description: 1992 SOA Annual Meeting, Washington, D.C. This session on individual life product update addressed Whole Life, Variable Life, VUL, and Term Life. within these products the discussion covered trends in product design, lapsation, and market trends.

Description: Panelists at this session of the SOA 1991 Toronto Spring Meeting give an overview of Regulation XXX including its effect on product design, proposed actuarial guidelines, 2001 CSO, X-factor analysis and reinsurance issues.

Description: This discussion is about the increasing unit expense rates even in light of increases in insurance amount, simplified procedures and greater mechanization, methods to allocate agency expenses in to the premium structure and the importance considerations in determining the level of gross premiums. From Transactions of Society of Actuaries 1961 Vol. 13 pt. 2

Description: This paper will not attempt to cover the details of every possible approximate method, but will endeavor to illustrate the use of principles andgeneral methods. It is hoped that the application of these principles and methods will help develop particular solutions to many of the varied valuation problems of today. From Transactions of Society of Actuaries 1955, Vol. 7, No. 19.

Description: Claims are insurers deepest and richest transactional data – yet we rarely unleash the full potential of this data. In this session, we will discuss potential sources and attributes of claims data, traditional approaches alongside new analytic techniques and how to integrate models into existing claims management processes. During this session, the presenters will discuss different areas of how machine learning can drive significant value unleashing the potential in claims data, these include data capture, claims management and workflow design as well as behaviour prediction. In addition, the audiences will also have opportunity to learn different use cases of how claims models taking advantage of machine learning, such as foreign death fraud detection and critical illness triage.