Current Issue

All articles published in this issue have undergone a thorough peer review process, and stringent checks for repetition rates have been implemented to ensure the integrity of the content.

Total number of articles in this issue: 2
Total number of pages in this issue: 29

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Year

2025

Volume

2

Number

4

Status

Archived

Published

2025 August 24

Articles

The Impact of Government Budget Data Visualization on Public Financial Literacy and Civic Engagement

This study explores the transformative power of data visualization technologies for public understanding and civic engagement in government budget processes. This material is based on a broad-based empirical study using a sample of 1,200 subjects from a variety of demographic groups in which we investigate how interactive visualization tools foster greater financial literacy and civic engagement relative to traditional budget presentation approaches. Our results show that visualization-enhanced budget information improves comprehension by 47% and willingness to be involved in civic activities by 32%. The methodology used in this study is a mixed-method that includes controlled experiments, cognitive evaluation instruments, and longitudinal surveys, and it focuses on the analysis of the relationship between visualization design elements and user engagement outcomes. Analysis shows that visual complexity is significantly related to user attributes and participatory actions. The current study is a part of emerging literature on digital governance and has implications for policymakers interested in promoting greater transparency and democratic participation in financial communication through technology.

DOI10.70393/6a6574626d.333133ARKark:/40704/JETBM.v2n4a01

Research on Cross-border Securities Anomaly Detection Based on Time Zone Trading Characteristics

Cross-border securities trading presents unique challenges in financial market surveillance, particularly regarding temporal patterns that span multiple time zones. This research proposes a novel approach for detecting anomalous trading behaviors in international securities markets by leveraging time zone-specific trading characteristics. The methodology combines advanced machine learning techniques with temporal feature extraction to identify suspicious activities from Asia-Pacific region investors trading in US markets. The study analyzes trading patterns across Hong Kong, Singapore, and Australian time zones, developing a comprehensive framework that captures cultural and temporal nuances in trading behaviors. Experimental validation demonstrates significant improvements in anomaly detection accuracy while reducing false positive rates compared to traditional methods. The proposed system achieves 94.7% precision and 91.3% recall in identifying cross-border trading anomalies, with particular effectiveness in detecting after-hours suspicious activities. The research contributes to enhanced financial market integrity through culturally-aware AI models that support regulatory compliance across international trading platforms.

DOI10.70393/6a6574626d.333134ARKark:/40704/JETBM.v2n4a02

Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
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