BLOCKCHAIN AND MACHINE LEARNING FOR FRAUD DETECTION: A PRIVACY-PRESERVING AND ADAPTIVE INCENTIVE BASED APPROACH

Abstract

Fraud detection has become increasingly
important in the digital era due to the rapid
growth of online transactions, financial
technologies, and e-commerce platforms.
Traditional fraud detection systems are
primarily centralized and rule-based,
which makes them vulnerable to data
breaches, limited scalability, and inability
to detect evolving fraud patterns. These
systems also face challenges related to data
privacy, as sensitive financial and personal
information is often stored and processed
in centralized databases. To address these
limitations, this paper proposes a novel
framework that integrates Blockchain
technology with Machine Learning to
develop a secure, decentralized, and
privacy-preserving fraud detection system.
The proposed system leverages blockchain
to provide a distributed and immutable
ledger, ensuring transparency, trust, and
data integrity among participants. Machine
learning algorithms are used to analyze
transaction data and identify suspicious
activities by detecting anomalies and
patterns indicative of fraud. Instead of
sharing raw data, the system utilizes
privacy-preserving techniques such as
distributed or federated learning, where
only model parameters or encrypted
insights are shared across the network.