An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions
Gunjan Mishra, Yash Mishra

TL;DR
This paper introduces an Adaptive Neuro-Fuzzy Blockchain-AI framework that enhances security and real-time fraud detection in FinTech transactions by combining blockchain transparency with adaptive learning models.
Contribution
It presents a novel integrated framework combining blockchain and neuro-fuzzy AI for adaptive, secure, and efficient FinTech transaction validation and fraud detection.
Findings
ANFB-AI outperforms recent algorithms in accuracy and precision.
The framework reduces transaction confirmation time and latency.
Simulations show effectiveness across various fraud scenarios.
Abstract
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Financial Distress and Bankruptcy Prediction · Imbalanced Data Classification Techniques
