Blockchain and Machine Learning for Fraud Detection: Employing Artificial Intelligence in the Banking Sector

Harnessing Blockchain and Machine Learning for fraud detection.

Published in Conference Proceedings:

GCU INTERNATIONAL KNOWLEDGE TRANSFER CONCLAVE - 2018 (ISBN 978-93-86516-46-6)

Best Paper Award Winner

Abstract

Fraudulent banking operations can cause huge losses to the bank and further affect the economy negatively. What if Blockchain Technology and Machine Learning could be combined to detect suspicious banking activity and stop transactions at the source? That is what this paper aims to do. In this paper, a system is created which consists of the following components:

1. Blockchain: To securely store transaction history.
2. XGBoosted KMeans algorithm: For quick and efficient detection of outliers, which indicate suspicious activity.
3. Apache Ignite: This is an open source platform that provides powerful computing for real-time Machine Learning.

Keywords: Machine Learning, Data Science, Artificial Intelligence, Kmeans Clustering, Blockchain, Apache.

Dataset

source: http://lisp.vse.cz/pkdd99/berka.html

Here's a data map describing each file's correlation.

View Paper: https://independent.academia.edu/VinitaSilaparasetty/Conference-Presentations

Author Comments: This paper was merely a surface study on Machine Learning and Blockchains as the time alloted to prepare and submit the paper was just one week. Feel free to point out any errors or make suggestions.