2021/11/05 by Jaime D. Acevedo-Viloria, Acevedo-Viloria, Jaime D., Sebastián Soriano Pérez +9
Computer Science · #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Spam and Phishing Detection #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.03707
Accepted for IEEE ISI 2021
arxiv created 2021/11/05 · openalex publication_date 2021/11/05 · arxiv updated 2021/11/09 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Identity theft is a major problem for credit lenders when there's not enough data to corroborate a customer's identity. Among super-apps large digital platforms that encompass many different services this problem is even more relevant; losing a client in one branch can often mean losing them in other services. In this paper, we review the effectiveness of a feature-level fusion of super-app customer information, mobile phone line data, and traditional credit risk variables for the early detection of identity theft credit card fraud. Through the proposed framework, we achieved better performance when using a model whose input is a fusion of alternative data and traditional credit bureau data, achieving a ROC AUC score of 0.81. We evaluate our approach over approximately 90,000 users from a credit lender's digital platform database. The evaluation was performed using not only traditional ML metrics but the financial costs as well.