Article: AI-enabled association rule mining with cloud platforms for rural digital finance services Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.40 pp.104 - 127 Abstract: This study presents an AI-enabled association rule mining framework integrated with cloud platforms to enhance rural digital finance services. The proposed system leverages scalable cloud infrastructure for large-scale transaction analysis, enabling efficient identification of patterns and relationships within rural financial data. Results demonstrate improved service personalisation, fraud detection, and strategic decision-making. Rural digital finance faces challenges in data management, transaction security, and service personalisation. Leveraging AI-driven association rule mining with cloud computing can bridge these gaps by enabling real-time, scalable analytics. Previous studies have explored cloud-based financial analytics and AI-driven transaction pattern discovery. Existing methods often lack adaptability for low-resource environments, preprocessing, and AI-driven association rule mining. Data from rural financial institutions is processed to uncover actionable patterns for decision-making. Experiments using large-scale rural transaction datasets achieved high pattern discovery accuracy and reduced processing time. The system demonstrated scalability and robustness under varying data loads. Inderscience Publishers - linking academia, business and industry through research

Title: AI-enabled association rule mining with cloud platforms for rural digital finance services

Authors: Yuanyuan Yue; Qianqian Wang

Addresses: Sichuan Vocational College of Finance and Economics, Chengdu, Sichuan, 610101, China ' Sichuan Vocational College of Finance and Economics, Chengdu, Sichuan, 610101, China

Abstract: This study presents an AI-enabled association rule mining framework integrated with cloud platforms to enhance rural digital finance services. The proposed system leverages scalable cloud infrastructure for large-scale transaction analysis, enabling efficient identification of patterns and relationships within rural financial data. Results demonstrate improved service personalisation, fraud detection, and strategic decision-making. Rural digital finance faces challenges in data management, transaction security, and service personalisation. Leveraging AI-driven association rule mining with cloud computing can bridge these gaps by enabling real-time, scalable analytics. Previous studies have explored cloud-based financial analytics and AI-driven transaction pattern discovery. Existing methods often lack adaptability for low-resource environments, preprocessing, and AI-driven association rule mining. Data from rural financial institutions is processed to uncover actionable patterns for decision-making. Experiments using large-scale rural transaction datasets achieved high pattern discovery accuracy and reduced processing time. The system demonstrated scalability and robustness under varying data loads.

Keywords: multimodal motion analysis; inertial motion sensors; recurrent neural networks; graph-based neural networks; action recognition; intelligent sports training.

DOI: 10.1504/IJICT.2025.149817

International Journal of Information and Communication Technology, 2025 Vol.26 No.40, pp.104 - 127

Received: 13 Aug 2025
Accepted: 19 Sep 2025
Published online: 13 Nov 2025 *

Title: AI-enabled association rule mining with cloud platforms for rural digital finance services

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