Detector

Random forest

Scheme (mathematics)

Statistics

Support vector machine

Money laundering

Telecommunications

Computer science

Filter (signal processing)

Business

Empirical research

Mathematics

Estimator

Sampling (signal processing)

Sampling scheme

Mathematical analysis

Artificial intelligence

Decision tree

Programming language

Database transaction

Finance

Artificial neural network

Financial institution

Machine learning

Computer vision

Computational Economics

This paper studies the interplay of machine learning and sampling scheme in an empirical analysis of money laundering detection algorithms. Using actual transaction data provided by a U.S. financial institution, we study five major machine learning algorithms including Bayes logistic regression, decision tree, random forest, support vector machine, and artificial neural network. As the incidence of money laundering events is rare, we apply and compare two sampling techniques that increase the relative presence of the events. Our analysis reveals potential advantages of machine learning algorithms in modeling money laundering events. This paper provides insights into the use of machine learning and sampling schemes in money laundering detection specifically, and classification of rare events in general.

Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection