An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions

Computer Science > Cryptography and Security [Submitted on 25 Mar 2026] An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions Gunjan Mishra, Yash Mishra Financial systems have a growing reliance on computer-based and distributed systems, making FinTech systems vulnerable to advanced and quickly emerging cyber-criminal threats. Traditional security systems and fixed machine learning systems cannot identify more intricate fraud schemes whilst also addressing real-time performance and trust demands. This paper presented an Adaptive Neuro-Fuzzy Blockchain-AI Framework (ANFB-AI) to achieve security in FinTech transactions by detecting threats using intelligent and decentralized algorithms. The framework combines both an immutable, transparent and tamper resistant layer of a permissioned blockchain to maintain the immutability, transparency and resistance to tampering of transactions, and an adaptive neuro-fuzzy learning model to learn the presence of uncertainty and behavioural drift in fraud activities. An explicit mathematical model is created to explain the transaction integrity, adaptive threat classification, and unified risk based decision-making. The proposed framework uses Proof-of-Authority consensus to overcome low-latency validation of transactions and scalable real-time financial services. Massive simulations are performed in normal, moderate, and high-fraud conditions with the use of realistic financial and cryptocurrency transactions. The experimental evidence proves that ANFB-AI is always more accurate and precise than recent state-of-the-art algorithms and costs much less in terms of transaction confirmation time, propagation delay of blocks and end-to end latency. ANFB-AI performance supports the appropriateness of adaptive neuro-fuzzy intelligence to blockchain-based FinTech security. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.23829 [cs.CR]   (or arXiv:2603.23829v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.23829 Focus to learn more Submission history From: Gunjan Mishra [view email] [v1] Wed, 25 Mar 2026 01:28:02 UTC (1,641 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)