Description: As the use of predictive models and novel datasets becomes more prevalent, a focus on consumer protection is being highlighted by regulators, ethicists, and industry insiders. Over time, increased rigor should and will be applied in model development and implementation. These issues are not new. Hundreds of individuals have worked on these issues in the laboratory, the board room, and the court room. The Civil Rights Act of 1964, Uniform Guidelines of the US EEOC, the Principles for the Validation and Use of Personnel Selection and other sources are intended to protect the rights of citizens and consumers and provide guidance to professionals in the development and use of decision-making tools. Drawing heavily on examples and parallels from psychology and employment practices as well as other disciplines, concepts such as model bias, fairness, adverse impact, disparate treatment and the like will be presented. Suggestions to ensure model validity, demonstrate model and data relevancy and minimize the potential for adverse impact to protected classes will also be offered. Each of these considerations will be linked to recent dialogue and concerns in the Life Insurance sector.

Description: The session will feature case study discussion of how insurers can improve their model architecture and data visualization to prepare for the increasing demands of the future. Actuarial data and analysis has long been limited by the complexity and diversity of historical systems. External pressure on insurers is higher than ever before, as new requirements take effect under PBR, GAAP Targeted Improvements, and IFRS17.

Description: This presentation is a Teaching Session, number 46TS, from the 2002 Valuation Actuary Symposium, held September 19-20 in Lake Buena Vista, FL. In this session, the instructor discusses simple tools and guidelines to ensure that correct inferences are drawn. The material is presented without using complex mathematical formulas. Topics include application of a normal distribution—when it is appropriate and when it is not, drawing inferences about and creating confidence intervals around tail percentile, and Monte Carlo Simulations for Regulation XXX.

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.

Description: 2003 Valuation Actuary Symposium, San Diego, CA. This session addressed the emerging practice where management is developing new ways to examine financial risks holistically in an integrated model.

Description: Transcript of discussion regarding rank correlation and bivariate distributions.

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: 1997 Valuation Actuary Symposium, Session 3 looks at considerations in strengthening reserves as a result of cash flow testing results.

Description: 1997 Valuation Actuary Symposium, Session 5 explores health cash-flow testing.

Description: This session at the 1998 Valuation Actuary Symposium discusses issues related to variable and fixed deferred annuity modeling.