Multi-task Learning Framework for Intelligent Risk Assessment in Global Digital Cultural Trade

Zihui Tian

doi:10.54254/2755-2721/2026.27391

Applied and Computational EngineeringOpen access

Multi-task Learning Framework for Intelligent Risk Assessment in Global Digital Cultural Trade

Research Article

Open Access

Multi-task Learning Framework for Intelligent Risk Assessment in Global Digital Cultural Trade

Zihui Tian ^1*

¹ New York University, New York, USA

^*Corresponding author: zt2406@nyu.edu

Published on 2 October 2025

ACE Vol.189

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-409-0

ISBN (Online): 978-1-80590-410-6

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Abstract

As global digital cultural trade expands, cross-border transactions face increasingly complex multidimensional risks, including policy compliance, cultural semantic adaptability, and transactional uncertainty. Traditional single-task risk assessment models struggle to capture these interrelated factors holistically. To address this gap, this study proposes an intelligent risk assessment framework based on multi-task learning that integrates heterogeneous data sources, including transactional records, policy documents, and cultural annotations. The model leverages a shared encoder and task-specific decoders to jointly predict three types of risks. Experiments conducted on a multimodal dataset collected from 2020 to 2024 demonstrate that the proposed framework outperforms single-task baselines across all tasks, with an average Macro-F1 improvement of approximately 12%. The cultural semantic risk task shows particularly strong performance in low-resource scenarios. These results confirm the effectiveness of the multi-task approach in enabling cross-task knowledge transfer and provide a scalable AI-driven solution for managing digital cultural trade risks in complex global environments.

Keywords:

Digital Cultural Trade, Risk Assessment, Multi-task Learning, Cross-cultural Data Mining, Intelligent Decision Systems

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References

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[11]. Assaf, Osama. Multivariate and multi-task deep learning architectures for improved stock market prediction and risk management. Diss. University of Reading, 2024.

References

[1]. Guo, Yanjun, Xinbo Ai, and Wenbin Luo. "A multi-task learning risk assessment method for the chemical process industry." Process Safety and Environmental Protection 186 (2024): 980-994.

[3]. Li, Xurui, et al. "Hierarchical Multi-task Learning for Enterprise Risk Detection from Financial Documents." 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022.

[6]. Ashford, Linda J., et al. "Statistical learning methods and cross-cultural fairness: Trade-offs and implications for risk assessment instruments." Psychological Assessment 35.6 (2023): 484.

[9]. Ponsard, Christophe, and Philippe Massonet. "Survey and Guidelines about Learning Cyber Security Risk Assessment." ICISSP. 2022.

[10]. Fichman, Pnina, Barbara Krumay, and Edward Bernroider. "Introduction to the Minitrack on Global, International, and Cross-Cultural Issues in the Digital Economy." (2021).

[11]. Assaf, Osama. Multivariate and multi-task deep learning architectures for improved stock market prediction and risk management. Diss. University of Reading, 2024.

Cite this article

Tian,Z. (2025). Multi-task Learning Framework for Intelligent Risk Assessment in Global Digital Cultural Trade. Applied and Computational Engineering,189,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of The 6th International Conference on Signal Processing and Machine Learning

ISBN: 978-1-80590-409-0(Print) / 978-1-80590-410-6(Online)

Editor: Marwan Omar

Conference website: https://www.confspml.org/

Conference date: 4 February 2026

Series: Applied and Computational Engineering

Volume number: Vol.189

ISSN: 2755-2721(Print) / 2755-273X(Online)

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