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Seminar on Computational Learning and Adaptation
On the 7th of December 2005, I had the opportunity to present at Stanford University about the "Business Impact of Predictive Analytics". I paste the Seminar on Computational Learning and Adaption announcement below. The seminar is chaired by Professor Pat Langley, and a number of members of his research staff and other guests also participated in the seminar.
The discussion during and after my presentation hit a couple of interesting points. One was that it would be interesting to explore the possibility of going beyond the prediction of a single target variable to predict sets of variables.
For example, a predictive capability might predict the risk of an event happening, the cost of either outcome of the event happening or not happening, the cost of taking action to prevent the event, and the probability that the action might prevent the event. This set of variables together would allow for a more comprehensive analysis of business impact.
For instance, in an insurance fraud prediction and prevention application, it is desirable to predict, for each claim, the probability of fraud, the predicted cost of fraud, the cost of taking action to prevent fraud, and the probability that the action will prevent payment on the fraudulent claim.
Seminar on Computational Learning and Adaptation
Business Impact of
Predictive Analytics
Dr. Tilmann Bruckhaus
Chief Architect, Data Mining and Analytics
Sun Microsystems
Tilmann.Bruckhaus@Sun.Com
In commercial applications of predictive modeling, the
ultimate objective is typically to maximize Return On Investment (ROI).
However, literature, conferences, and training often
stops short of providing techniques for ROI maximization. With an
apparent lack of know-how for maximizing ROI, analysts often have to
rely on technical metrics, such as ROC, accuracy, precision, or similar
metrics to optimize predictive models. In this presentation, I will
explore the problem of assessing the ROI for predictive analytics
applications, break down the drivers of ROI, and show how to compute
ROI. I will also present an example ROI analysis to demonstrate that
one predictive model can have negative or positive ROI based on the
business context in which it is used, even though technical quality
metrics of the predictive model do not change.
|
Date: Wed., Dec 7, 2005 |
Time: 4:15-5:30PM |
Place: Cordura 100 |
Return to the seminar schedule