MATH THEORY OF FRAUD DETECTION

Mathematical Theory of Fraud Detection

presents a rigorous, quantitative alternative:

a unified probabilistic framework for identifying and scoring fraud across the full lifecycle of a lead, from publisher and campaign to consumer, device, and buyer.

Authored by a Professor of mathematics, computer science and expert with decades of combined experience in math and modeling.

Grounded in Bayesian inference, Dynamic Bayesian Networks, and modern AI methods, this book treats fraud not as a binary label but as a latent state that evolves over time and must be inferred from noisy, observable evidence — IP behavior, mouse and keystroke dynamics, browser fingerprints, and conversion patterns.