If you have any problems related to the accessibility of any content (or if you want to request that a specific publication be accessible), please contact (scholarworks@unr.edu).
BCReporter: A Real-Time, Machine Learning, Fraud Detection Platform
Date
2017Type
ThesisDepartment
Computer Science and Engineering
Degree Level
Honors Thesis
Degree Name
Computer Science
Abstract
Bristlecone Holdings analyzes business finances and customer accounts using data files exported from the third party software program Tableau. In order to detect fraudulent applications, these data files are analyzed by Bristlecone data analysts using a manual and tedious process that is often inconsistent in producing useful results. The company has been growing significantly in recent years and therefore requires an easier and more automated system to perform this fraud detection analysis. The creation of this automated system was the focus of Team Bristlecone this past year and has been titled the BC Reporter tool. The system consists of a collection of configurable tools, including an automated file analyzer and a user friendly web interface. The system was developed in a modular and configurable fashion, allowing it to evolve to meet the ever changing demands of nearly any modern business. The system currently exists as a usable product, but can be still expanded upon and improved upon greatly.
Permanent link
http://hdl.handle.net/11714/1882Additional Information
Rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 United States |
---|---|
Rights Holder | Author(s) |