International Journal on Science and Technology

E-ISSN: 2229-7677     Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 4 October-December 2025 Submit your research before last 3 days of December to publish your research paper in the issue of October-December.

Safeguarding Digital Finance from Frauds using ML Technologies in Blockchain Technology

Author(s) Ms. Asha Sri Nimmaka, Prof. Venkata Rao K
Country India
Abstract The rapid digitization of financial services has resulted in a staggering increase in sophisticated fraud, endangering global economies and damaging public trust. The dynamic nature of current fraud is outpacing classic fraud detection systems, which frequently rely on static, rule-based methods. This study reveals a new hybrid framework that pairs distributed ledger technology for immutable transaction avoidance with Machine Learning (ML) for real-time fraud detection. The fundamental driving force is to address the inherent shortcomings of centralized systems, as well as the lack of an unchangeable audit trail in ML-only solutions. Using a range of classification algorithms, our methodology entails creating separate machine learning pathways for three important financial domains: credit card, UPI, and loan applications. A fraud verdict is subsequently produced using the top-performing model for each domain, which is determined by a thorough analysis of metrics. Through a smart contract, this decision is safely and irrevocably documented on a private blockchain. This study shows how a strong security architecture may be produced by fusing the decentralized trust and immutability of blockchain technology with the predictive performance of machine learning. The findings demonstrate that this integrated approach strengthens the integrity and dependability of digital financial transactions by achieving high performance in fraud detection as well as creating a transparent and impenetrable record.
Keywords Machine Learning, Private Blockchain, Hybrid framework, Smart contract
Field Engineering
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-10-19
DOI https://doi.org/10.71097/IJSAT.v16.i4.8906
Short DOI https://doi.org/g97s6s

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