Adaptive Financial Fraud Detection in Imbalanced Data with Time-Varying Poisson Processes

Abstract

This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes. The probability of predicting fraud on the financial transaction is derived. Applying our methodology to the financial dataset shows a better predicting power than a baseline approach, especially in the case of higher imbalanced data.

Accepted for publication in the Journal Of Financial Risk Management (JFRM). Comments welcome