Enabling Fraud Prediction on Preliminary Data Through Information Density Booster

• Published in: IEEE transactions on information forensics and security Vol. 18; p. 1
• Main Authors: Zhu, Hangyu, Wang, Cheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Boosting; Data mining; Density; Fraud; Fraud prediction; History; information density; Machine learning; network representation learning; online lending services; Representation learning; Risk management; Semantics; Task analysis
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2023.3300523

In online lending services, fraud prediction is an especially critical step to control loss risk and improve processing efficiency. Unfortunately, it is definitely challenging since the ex-ante prediction actually needs to be made only based on the most basic information of applicants. This work figures out that the essential difficulty here is the low information density of data associations which contain the useful information for fraud prediction. Accordingly, we propose a novel multi-stage data representation scheme, called AI2Vec (Applicant Information Vectoring), as an information density booster. It can gradually boost information density of associations by simultaneously decreasing the scale of information carriers and increasing the amount of useful information. The qualified performance of our AI2Vec is validated by the experiments over real-life data from a prestigious online lending platform. It can help commonly-used machine learning classifiers outperform the state-of-the-art methods, including the method of pilot platform with manual feature engineering by the subject matter experts.