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Abstract Details

Activity Number: 14
Type: Topic Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract - #306099
Title: Statistical Modeling for Large Financial Data Sets
Author(s): Antonello Loddo*+
Companies: Capital One
Address: 3200 Shandwick Pl., Fairfax, VA, 22031, United States
Keywords: cunsumer financial ; statistical modeling ; large data

Statistical modeling for the consumer financial industry presents some unique characteristics. Large sample sizes and focus on predictive modeling of complex behaviors make for a typical directed data mining problem. However, models used for credit decisions might require treatment conditional predictions. This, coupled with interpretability requirements, render some supervised learning approaches unfeasible. In addition, economic knowledge, econometric methodology, business insights and additional information not included in the data can be used to improve model performance. This study focuses on these aspects of modeling consumer financial data, and how statisticians can leverage them in order to build better models.

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