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Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Secure and Intelligent Financial Data Analysis Using Machine Learning, Fuzzy Logic, and Cryptography

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pp. 2163–2173Vol. 28Issue 5-BAugust 2025DOI: 10.47974/JDMSC-2432 Crossmark XML
Received:
05 Nov 2024
Published Online:
30 Aug 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2432
Pages:
2163–2173

Abstract

This paper suggests using machine learning (ML), fuzzy logic and cryptographic techniques together to form a complete framework for predicting financial risks. The goal is to perform accurate, understandable and safe analysis that determines financial risk, for example, in credit scoring or determining if a loan will be granted. Machine learning is used to spot patterns in past and current financial data and fuzzy logic explains decisions and handles the risks that are uncertain. Cryptographic methods named AES, RSA and federated learning make sure security is maintained in the updates. Fuzzy-based access control is supported by the framework which makes it easy to change data access based on people’s roles and actions. Results of the experiments prove that the principal can predict accurately, score risks according to experts’ views and protect the data and accounts.

Keywords

Subject Classifications

Primary 68T05Secondary 94A6091B30

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