Activity Number:
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67
- Section on Statistical Computing: Data Science
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #313811
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Title:
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A Simple and Scalable Algorithm for Anomaly Identification with an Application in Credit Card Fraud Detection
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Author(s):
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Cheng Peng*
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Companies:
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West Chester University of Pennsylvania
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Keywords:
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Anomaly Identification;
Machine Learning Algorithm;
Bootstrap;
Process Capability Index;
Control Charting;
Fraud Detection
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Abstract:
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We present a simple and scalable algorithm for anomaly identification using the control chart based on the process capability indices of any processes that generate numeric characteristics in a sequential manner. The algorithm can be considered as a machine learning algorithm and used standalone for anomaly identification or classification. It can also be used as a method of feature extraction that aggregates information from sequence data. A simple version of the algorithm has been successfully implemented in a real-time production environment for credit card fraud detection. As an example, I will present an application of the algorithm as a fraud detection tool on a static credit card transaction data.
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Authors who are presenting talks have a * after their name.