Graduation Year

2017

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Computer Science

Reader 1

Robert Keller

Reader 2

Christopher Towse

Rights Information

© 2017 Sneha R Deo

Abstract

Over the past decade, developments in data analysis have improved the quality and efficiency of fraud detection software. Unfortunately, malicious behaviors have also become more subtle, and the quantity of financial data requires techniques to be both accurate and efficient on large-scale and quickly-changing data sets. To address this problem space, the FICO Clinic team was tasked with identifying rare behaviors in unannotated transactional data using topic models. This thesis will describe the development of various novel topic modelling approaches for the detection the latent behaviors that indicate rare events and the methods for their evaluation. Excluded are the results of the project and a description of the novel algorithms, which are the intellectual property of FICO.

This thesis is restricted to the Claremont Colleges current faculty, students, and staff. It is not available for interlibrary loan. Please send a request for access through Contact Us.

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