Awards

This award is made to the authors of the best papers submitted in response to a call for data management/data quality discussion papers whenever the program is conducted by the Committee on Management Data and Information of the Casualty Actuarial Society. Papers are judged by a specially appointed review committee on the basis of originality of ideas, understandability of complex concepts, contribution to the literature, and thoroughness of ideas expressed. If no paper is considered eligible in a given year, the award shall not be made. The committee's decision will be final. Recipients need not be members of the Casualty Actuarial Society. The announcement of the award will be made at the seminar at which the papers are presented. The amount of the Management Data and Information Prize is determined annually.

Louise Francis FCAS, MAAA. "Dancing with Dirty Data" March 2005
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Abstract
Much of the data that actuaries work with is dirty. That is, the data contain errors, miscodings, missing values and other flaws that affect the validity of analyses performed with such data. This paper will give an overview of methods that can be used to detect errors and remediate data problems. The methods will include outlier detection procedures from the exploratory data analysis and data mining literature as well as methods from research on coping with missing values. The paper will also address the need for accurate and comprehensive metadata. Conclusions. A number of graphical tools such as histograms and box and whisker plots are useful in highlighting unusual values in data. A new tool based on data spheres appears to have the potential to screen multiple variables simultaneously for outliers. For remediating missing data problems, imputation is a straightforward and frequently used approach

Availability. The R statistical language can be used to perform the exploratory and cleaning methods described in this paper. It can be downloaded for free at http://cran.r-project.org/.

Louise Francis FCAS, MAAA. "Martian Chronicles: Is MARS better than Neural Networks?" March 2003.
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Abstract:
This paper will introduce the neural network technique of analyzing data as a
generalization of more familiar linear models such as linear regression. The reader is introduced to the traditional explanation of neural networks as being modeled on the functioning of neurons in the brain. Then a comparison is made of the structure and function of neural networks to that of linear models that the reader is more familiar with.

The paper will then show that backpropagation neural networks with a single hidden layer are universal function approximators. The paper will also compare neural networks to procedures such as Factor Analysis which perform dimension reduction. The application of both the neural network method and classical statistical procedures to insurance problems such as the prediction of frequencies and severities is illustrated.

One key criticism of neural networks is that they are a "black box". Data goes into the "black box" and a prediction comes out of it, but the nature of the relationship between independent and dependent variables is usually not revealed.. Several methods for interpreting the results of a neural network analysis, including a procedure for visualizing the form of the fitted function will be presented.

Louise Francis, FCAS, MAAA. "Neural Networks Demystified." March 2001.
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Abstract:
This paper will introduce the neural network technique of analyzing data as a
generalization of more familiar linear models such as linear regression. The reader is introduced to the traditional explanation of neural networks as being modeled on the functioning of neurons in the brain. Then a comparison is made of the structure and function of neural networks to that of linear models that the reader is more familiar with.

The paper will then show that backpropagation neural networks with a single hidden layer are universal function approximators. The paper will also compare neural networks to procedures such as Factor Analysis which perform dimension reduction. The application of both the neural network method and classical statistical procedures to insurance problems such as the prediction of frequencies and severities is illustrated.

One key criticism of neural networks is that they are a "black box". Data goes into the "black box" and a prediction comes out of it, but the nature of the relationship between independent and dependent variables is usually not revealed.. Several methods for interpreting the results of a neural network analysis, including a procedure for visualizing the form of the fitted function will be presented.

 

This award, which commemorates the work of Gustav F. Michelbacher, is made to the author of the best paper submitted in response to a call for discussion papers whenever the program is conducted by the Casualty Actuarial Society. Papers are judged by a specially appointed committee on the basis of originality, research, readability, completeness, and other factors. If no paper is considered eligible in a given year, the award shall not be made. The committee's decision will be final. Recipients need not be members of the Casualty Actuarial Society. The announcement of the award will be made at the meeting at which the papers are discussed.

Louse Francis FCAS, MAAA. "A Model for Combining Timing, Interest Rate and Aggregate Risk Loss." 1998.
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Abstract:
The purpose of this paper is to develop a simple model for determining distributions of present value estimates of aggregate losses. Three random components of the model that will be described are aggregate losses, payout patterns, and interest rates. In addition, this paper addresses the impact of timing and investment variability on risk margin/solvency requirements.