The Impact of Cross-Border Data Flows on Digital Trade: A Comparative Analysis of the Benefits and Challenges for International Trade and Economic Development

Reshma Soman N · S. Dinesh Kumar · H. Sakthi Vadivelan · T Kowshik · Praveen Anandhan · Subhadra Muthuramanathan

0 citations0/0 files storedInternational Conference on Information Systems and Computer Networks (ISCON)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

E-commerce and Technology Innovations Cybercrime and Law Enforcement Studies Digital Economy and Transformation

Abstract

Digital trade regulations that focus on cross-border data flow have given nations benefits in contemporary international competitiveness and collaboration. Nevertheless, due to the complexity of international legislation and the lack of adequate privacy legislation, developing nations like India face specific challenges in managing these flows. Digital trade tactics have undergone significant changes in tandem with the growth of communication technology and its impact on trade trends. Machine learning (ML) methods must be used to automate a variety of procedures and advance digital trade to adjust to these shifts. This study presents an ML-based system for identifying challenges in cross-border international digital trade that supports India's economic development. The system tackles the intricate problems in digital trade taxes by combining multilevel detection methods with sophisticated feature development approaches. This study has identified and created new corporate structures and behavioral paradigms through the analysis of cross-border company data. Claims to have substantial regulatory status and to have a 95.7% validation accuracy with a 4.4% FP. The large market variation of the border trade may be monitored in real time because of its 0.2-second transaction effectiveness. According to research findings, the system consistently performs well in detecting intricate financial fraud trends and is stable across a range of operating settings and anomalous kinds. As novel deception trends appear, the platform's adaptable learning algorithms guarantee its continuous efficacy. By offering revenue agencies flexible systems for tracking cross-border international digital trade, the study advances both conceptual knowledge and real-world uses in tax management for digital trade. Comprehensive validation of the system's efficiency confirms its usefulness in actual tax management situations.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 0 citations exist upstream.

References

2020Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology 2020Machine learning-based e-commerce platform repurchase customer prediction model 2022Governing Cross-Border Data Flows: International Trade Agreements and Their Limits 2022Development of a risk index for cross-border data movement 2024THE INTERSECTION OF ARTIFICIAL INTELLIGENCE AND INTERNATIONAL TRADE LAWS: CHALLENGES AND OPPORTUNITIES 2021Application Analysis of Artificial Intelligence Technology in the Development of Cross-border E-commerce

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.