TY - JOUR
T1 - A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces
AU - Mutemi, Abed
AU - Bacao, Fernando
N1 - Mutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5
PY - 2023/8/2
Y1 - 2023/8/2
N2 - Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.
AB - Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.
UR - http://www.scopus.com/inward/record.url?scp=85166436245&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001042088200011
U2 - 10.1038/s41598-023-38304-5
DO - 10.1038/s41598-023-38304-5
M3 - Article
C2 - 37532696
SN - 2045-2322
VL - 13
SP - 1
EP - 16
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 12499
ER -