Dissertation/Thesis Abstract

Modernizing Check Fraud Detection with Machine Learning
by Rose, Lydia M., M.S., Utica College, 2018, 64; 13421455
Abstract (Summary)

Even as electronic payments and virtual currencies become more popular, checks are still the nearly ubiquitous form of payment for many situations in the United States such as payroll, purchasing a vehicle, paying rent, and hiring a contractor. Fraud has always plagued this form of payment, and this research aimed to capture the scope of this 15th century problem in the 21st century. Today, counterfeit checks originating from overseas are the scourge of online dating sites, classifieds forums, and mailboxes throughout the country. Additional frauds including alteration, theft, and check kiting also exploit checks. Check fraud is causing hundreds of millions in estimated losses to both financial institutions and consumers annually, and the problem is growing. Fraud investigators and financial institutions must be better educated and armed to successfully combat it. This research study collected information on the history of checks, forms of check fraud, victimization, and methods for check fraud prevention and detection. Check fraud is not only a financial issue, but also a social one. Uneducated and otherwise vulnerable consumers are particularly targeted by scammers exploiting this form of fraud. Racial minorities, elderly, mentally ill, and those living in poverty are disproportionately affected by fraud victimization. Financial institutions struggle to strike a balance between educating customers, complying with regulations, and tailoring alerts that are both valuable and fast. Applications of artificial intelligence including machine learning and computer vision have many recent advancements, but financial institution anti-fraud measures have not kept pace. This research concludes that the onus rests on financial institutions to take a modern approach to check fraud, incorporating machine learning into real-time reviews, to adequately protect victims.

Indexing (document details)
Advisor: Choo, Kyung-Seok
Commitee: McWhirt, Michael
School: Utica College
Department: Economic Crime Management
School Location: United States -- New York
Source: MAI 58/03M(E), Masters Abstracts International
Subjects: Criminology, Banking, Artificial intelligence
Keywords: Banks, Behavioral alerts, Credit unions, False positive, Financial crime and compliance management, Payment fraud
Publication Number: 13421455
ISBN: 978-0-438-71248-5
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