Understanding the Financial Transaction Security through Blockchain and Machine Learning for Fraud Detection in Data Privacy and Security

Abstract

This study examines the integration of blockchain technology and machine learning (ML) to enhance financial transaction security, with a focus on fraud detection, data privacy, and operational transparency.The study explores the combined capabilities of blockchain's decentralized ledger and ML's predictive analytics in securing financial transactions.A systematic review was conducted, sourcing relevant studies from academic databases where literature resources are stored, such as IEEE Xplore, Google Scholar, Scopus, Web of Science, DOAJ, and SCImago.3037 study papers were collected from those academic databases.After screening and testing eligibility, 137 papers were selected to conduct this study.Studies covering blockchain, ML, and their collaborative impact on financial security were selected, classified, and analyzed.Comparative analysis methods highlighted both the strengths and limitations of this dual-technology approach.Results indicate that blockchain's immutability and transparency, alongside ML's data-driven fraud detection capabilities, create a robust framework for transaction security.Blockchain effectively ensures data integrity and transparency, while ML algorithms improve fraud detection and decision-making through real-time data analysis.However, challenges such as scalability, high energy consumption, and high implementation costs persist, limiting adoption in small and medium-sized institutions.The combined application of blockchain and ML presents a transformative potential for financial sectors, particularly in enhancing transaction integrity, regulatory compliance, and risk management.This framework can serve as a model across various industries beyond finance, including government and non-financial organizations, to foster a secure transaction environment.This study primarily relies on qualitative data and lacks empirical validation through quantitative measures.Further, blockchain's energy-intensive nature and ML's data dependency pose obstacles to widespread implementation, especially in resource-constrained settings.Future research should aim at developing costeffective and energy-efficient blockchain and ML solutions to support broader adoption.Additionally, advancements in quantum computing and AI-driven blockchain could address existing security vulnerabilities, making the technology more accessible and scalable.