Abstract
In this thesis, real data from real fraud cases was leveraged to train a machine learning model. With the access to real data, model validation was conducted to prove the model was able to identify fraudulent transactions that it had never seen before leveraging patterns derived from Benford's Law. This law has proven powerful in recognizing patterns of numerical data that are un-natural. This set of work expands on the known powerful capabilities of Benford's Law by layering in related features that help to make the system able to recognize patterns in transactions very effectively. The overall objective is to focus large sets of data that contain very few fraudulent transactions down to a manageable number of high risk transactions to be further investigated.
Advisor
Visa, Sofia
Department
Computer Science
Recommended Citation
Mitchell, Grant, "Predicting Fraud in Accounts Payable using Benford's Law and Neural Networks" (2024). Senior Independent Study Theses. Paper 11127.
https://openworks.wooster.edu/independentstudy/11127
Disciplines
Computer Sciences | Data Science | Mathematics
Publication Date
2024
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
© Copyright 2024 Grant Mitchell