CREDIT CARD FRAUD DETECTION USING FUZZY LOGIC AND NEURAL NETWORK

Abstract

Billions of dollars of loss are caused every year by fraudulent credit card transactions.
The design of efficient fraud detection algorithms is key for reducing these losses, and
more and more algorithms rely on advanced machine learning techniques to assist fraud
investigators. The design of fraud detection algorithms is however particularly challenging
due to the non-stationary distribution of the data, the highly unbalanced class distributions,
and the availability of few transactions labeled by fraud investigators. At the same time,
public data are scarcely available for confidentiality issues, leaving unanswered many
questions about what the best strategy is. In this thesis, we aim to provide some answers
by focusing on crucial issues such as) why and how under-sampling is useful in the
presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how
to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution
and change of spending behavior), iii) how to assess performances in a way which is
relevant for detection and iv) how to use feedbacks provided by investigators on the fraud
alerts generated. Finally, we design and assess a prototype of a Fraud Detection System
able to meet real-world working conditions and that can integrate investigators’ feedback
to generate accurate alerts.