AI + Big Data: Transforming Traditional Credit Reporting

Yuchi Chen; Yifei Pei

doi:10.54254/2754-1169/2025.BL29505

Advances in Economics, Management and Political SciencesOpen access

AI + Big Data: Transforming Traditional Credit Reporting

Research Article

Open Access

AI + Big Data: Transforming Traditional Credit Reporting

Yuchi Chen ¹, Yifei Pei ^2*

¹ Beijing Normal University-Hong Kong Baptist University

² Tianjin University of Commerce

^*Corresponding author: peiyifei@stumail.tjcu.edu.cn

Published on 11 November 2025

AEMPS Vol.239

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-525-7

ISBN (Online): 978-1-80590-526-4

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Abstract

Against the background of a traditional credit system that can no longer meet today’s needs, this paper systematically analyzes its structural shortcomings, including one-dimensional data sources, outdated information updates, and severe data fragmentation, which explains the reason that it hampers inclusive-finance development. On this basis, it then explores how artificial intelligence and big-data technologies can be applied to credit reporting through three key relationship-network construction, including the use of relationship networks, full-dimension credit profiling, and dynamic risk alerts to improve both assessment accuracy and real-time timeliness. The study further highlights multiple risks in AI-driven credit systems, such as legal compliance challenges, privacy leakage, and model explainability risks inherent in AI-driven credit scoring. Accordingly, it proposes optimizing and governing a multi-level governance framework through the three layers: relationship networks, full-dimension profiling, and dynamic alerts, to push the industry toward greater precision, inclusiveness, and a secure development path for the credit industry.

Keywords:

Traditional credit reporting, AI credit reporting, Big data, Credit assessment, Risk prediction

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References

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[2]. Doğuş Emin, A. A. E. (2025). The role of AI in credit risk assessment: Evidence from OECD and BRICS via system GMM and random forest. Finance Research Letters, 81, 107499. https: //doi.org/10.1016/j.frl.2025.107499

[3]. Bao, W.Q., Xu, K., & Leng, Q. (2024). Research on the financial credit risk management model of real estate supply chain based on GA-SVM algorithm: A comprehensive evaluation of AI model and traditional model. Procedia Computer Science, 243, 900–909. https: //doi.org/10.1016/j.procs.2024.09.108

[4]. Wang, H.B. (2021). Credit risk management of consumer finance based on big data. Mobile Information Systems.

[5]. Zhou, L., Gu, R. P., Xing, X., & Wu, H.Y. (2022). A review and prospect of frontier research on big data credit reporting. Credit Reference, (1), 36–44.

[6]. Zhang, C. (2022). Research on credit data sharing mechanism based on blockchain. Jilin Financial Research, (01), 60–64.

[7]. Khashman, A. (2010). Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications, 37(9), 6233-6239.

[8]. Ghatasheh, N. (2014). Business analytics using random forest trees for credit risk prediction: a comparison study. International Journal of Advanced Science and Technology, 72(2014), 19-30.

[9]. Wang, F. P. (2018). Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA‐BP Neural Network. Complexity, 2018(1), 7616537.

[10]. Chen, L. Q. (2025). Credit reporting innovation from the perspective of new-quality productive forces: Exploring the path of finance-technology integration. Credit Reference, (4), 21–29.

References

[4]. Wang, H.B. (2021). Credit risk management of consumer finance based on big data. Mobile Information Systems.

[5]. Zhou, L., Gu, R. P., Xing, X., & Wu, H.Y. (2022). A review and prospect of frontier research on big data credit reporting. Credit Reference, (1), 36–44.

[6]. Zhang, C. (2022). Research on credit data sharing mechanism based on blockchain. Jilin Financial Research, (01), 60–64.

[7]. Khashman, A. (2010). Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications, 37(9), 6233-6239.

[8]. Ghatasheh, N. (2014). Business analytics using random forest trees for credit risk prediction: a comparison study. International Journal of Advanced Science and Technology, 72(2014), 19-30.

[9]. Wang, F. P. (2018). Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA‐BP Neural Network. Complexity, 2018(1), 7616537.

[10]. Chen, L. Q. (2025). Credit reporting innovation from the perspective of new-quality productive forces: Exploring the path of finance-technology integration. Credit Reference, (4), 21–29.

Cite this article

Chen,Y.;Pei,Y. (2025). AI + Big Data: Transforming Traditional Credit Reporting. Advances in Economics, Management and Political Sciences,239,22-27.

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 ICFTBA 2025 Symposium: Data-Driven Decision Making in Business and Economics

ISBN: 978-1-80590-525-7(Print) / 978-1-80590-526-4(Online)

Editor: Lukášak Varti

Conference website: https://2025.icftba.org/Bratislava.html

Conference date: 12 December 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.239

ISSN: 2754-1169(Print) / 2754-1177(Online)

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