Description: Up until recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops . In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents a promising prospect for overcoming problems of big data.

Description: This article goes through some basic data exploration techniques to show how different approaches look.

Description: This paper is a proof of a conjecture that appeared in a letter to the Editor of The Actuary. From the Actuarial Research Clearing House 1991 Vol.

Description: Presented at May 1985 Spring Meeting. This session discussed the differences in the reporting of product line and source of earnings results between insurers and banks and the practical problems which exist in interpreting gain by source or product-level.

Description: Panel discussion about ways can insurance carriers and health care coalitions work together, what data are coalitions requesting from health carriers, how health carriers respond to those requests, and implications for health carriers.

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: A review of the Bridging the Gap Series: Application of Predictive Modeling in VA/FIA Risk Management at the Equity Based Guarantees Conference in Chicago.

Description: A description of the Technology Section's goals and accomplishments.

Description: Letter from the editor describing the contents of the issue.

Description: Can we improve upon traditional methods of risk measurement and risk selection? Panelists discuss potential applications of predictive modeling, including individual underwriting, group rating and provider reimbursement. They focus on ways in which predictive modeling can be used to improve risk selection, experience rating and provider reimbursement. A session at the Health/Pension Spring Meeting held in New Orleans, Louisiana June 15-17, 2005. From the Record of the Society of Actuaries, Vol. 31, No. 2.