An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| author | Mishra, Gunjan Mishra, Yash |
| author_facet | Mishra, Gunjan Mishra, Yash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23829 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions Mishra, Gunjan Mishra, Yash Cryptography and Security 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. |
| title | An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2603.23829 |