Multi-Task Generative Financial Knowledge Graph Construction from Corporate ESG Disclosures and Green Financing Cost Prediction

Suwan Hu

doi:10.54254/2755-2721/2026.27392

Applied and Computational EngineeringOpen access

Multi-Task Generative Financial Knowledge Graph Construction from Corporate ESG Disclosures and Green Financing Cost Prediction

Research Article

Open Access

Multi-Task Generative Financial Knowledge Graph Construction from Corporate ESG Disclosures and Green Financing Cost Prediction

Suwan Hu ^1*

¹ Monash University, Melbourne, Australia

^*Corresponding author: rara481846778@gmail.com

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

Rapid population ageing in East Asia is reshaping family risk, especially in “4-2-1” households where one working-age adult bears layered financial and caregiving burdens for two parents and four grandparents. This study develops a composite Eldercare Vulnerability Index (EVI) that integrates health, economic, and social exposures and links it to a three-tier Policy Support Score (PSS) spanning national, provincial/prefectural, and municipal programmes. Using harmonised 2023 micro-surveys from China, Japan, and South Korea (N = 12,437), we estimate country-adjusted effects with Bayesian hierarchical models, validate the index against objective hardship events and caregiver stress, and run counterfactual policy simulations. Median baseline EVI differs across countries and displays heavy-tail heterogeneity within countries. Municipal in-kind services, particularly home-based care hours and daycare vouchers, exert larger marginal reductions in EVI than equivalently valued national cash transfers, with positive complementarities across tiers. Scenario analyses indicate that modest local expansions produce outsized, targetable improvements among high-risk clusters. The framework offers a replicable, policy-sensitive diagnostic to guide multilevel reform in rapidly ageing cities.

Keywords:

Financial knowledge graph, ESG disclosure, Generative language model, Multi-task learning, Green financing cost

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References

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References

[1]. Li, V. X. (2023). Findkg: Dynamic knowledge graph with large language models for global finance. Available at SSRN 4608445.

[4]. Yuan, W., Neubig, G., & Liu, P. (2021). Bartscore: Evaluating generated text as text generation. Advances in neural information processing systems, 34, 27263-27277.

[7]. Benghiat, S., & Lahmiri, S. (2025). Evaluating Predictive Models for Three Green Finance Markets: Insights from Statistical vs. Machine Learning Approaches. Computation, 13(3), 76.

Cite this article

Hu,S. (2025). Multi-Task Generative Financial Knowledge Graph Construction from Corporate ESG Disclosures and Green Financing Cost Prediction. Applied and Computational Engineering,189,8-13.

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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