Dyzma, M. (2018). Fraud Detection with Machine Learning: How Banks and Financial Institutions Leverage AI.
https://www.netguru.com/blog/fraud-detection-with-machine-learning-how-banks-and-financial-institutions-leverage-ai

has been cited by the following article:

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

  AUTHORS: Régis Houssou, Jérôme Bovay, Stephan Robert

  KEYWORDS: Homogeneous Poisson Process, Inhomogeneous Poisson Process, Intensity Model, Fraud Detection, Imbalanced Data

  JOURNAL NAME: Journal of Financial Risk Management, Vol.8 No.4, December 30, 2019

  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 financial datasets with different fraud profiles shows a better predicting power than a baseline approach, especially in the case of higher imbalanced data.