< Back to previous page

Project

Machine learning for fraud analytics

Fraud remains a major challenge for businesses. The Association of Certified Fraud Examiners (ACFE) estimates that a typical organization loses 5% of its revenues due to fraud. Furthermore, fraudsters continuously adapt their techniques in response to fraud detection efforts, creating a need for adaptive fraud detection systems. Given the abundant availability of data, machine learning techniques seem well suited to tackle this problem. Therefore, the goal is to develop a profit-driven fraud detection system that is able to swiftly adapt to changes in fraudulent behavior by continuously learning from the data. Additionally, given the complexity of real-life organizations, this system should be able to incorporate multiple levels of information. Therefore, the focus will particularly be on ensemble meta-learning schemes. These are observed to be amongst the most powerful supervised learning techniques, while being innately adaptive. Moreover, by customizing the objective function, they can be made cost-sensitive. These new techniques will be evaluated empirically with real-life datasets. This way, the project aims to add to the existing literature on profit-driven analytics, cost-sensitive learning, robust statistics and user and entity behavior analytics for fraud detection.

Date:27 Aug 2020 →  Today
Keywords:Fraud, Machine learning, Data analytics, Ensemble learning, Online learning, Multilevel learning
Disciplines:Statistics, Machine learning and decision making, Decision support and group support systems, Business information management
Project type:PhD project