Hierarchical Reinforcement Learning for Fraud Detection

Sun, 04 May 2025 00:00:00 +0000

What is this?

This was my final project for CS5841 (Machine Learning), built with three other students while I was studying at Michigan Technological University (MTU). I was the only undergraduate in the group, held my own, and am happy to present the work here.

The team

The report and the work are not mine alone — this was a team project, and everyone below deserves equal credit:

Amirmehdi Moghadamfarid
Samuel Raber
Eric Fosu-Kwabi

Hierarchical Reinforcement Learning Fraud Detection (HRLFD): an adaptive approach

Introduction

Financial fraud detection is a critical task in the modern digital economy. Traditional supervised methods often struggle with imbalanced datasets, where fraudulent transactions are far less common than legitimate ones. This project explores the use of reinforcement learning (Q-learning) combined with decision tree classifiers to detect fraudulent cases more effectively. The goal was to design and evaluate a hybrid model capable of identifying fraud while minimizing false negatives.

Uni Project on Niklas Lange

Hierarchical Reinforcement Learning for Fraud Detection

What is this?

The team

Hierarchical Reinforcement Learning Fraud Detection (HRLFD): an adaptive approach

Introduction