Artificial Intelligence in Full-Cycle Risk Management for Technology Credit: Mechanisms, Challenges, and Institutional Adaptation

Zhi Zheng; Guoqing Chen

doi:10.47297/wspceWSP2516-251932.20261004

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Artificial Intelligence in Full-Cycle Risk Management for Technology Credit: Mechanisms, Challenges, and Institutional Adaptation

Volume 10, Issue 4

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Artificial Intelligence in Full-Cycle Risk Management for Technology Credit: Mechanisms, Challenges, and Institutional Adaptation
Artificial Intelligence in Full-Cycle Risk Management for Technology Credit: Mechanisms, Challenges, and Institutional Adaptation
School of Finance, Chengdu Jincheng College, Chengdu, Sichuan, 611731, China
Vol. 10, Issue 4, Pages: 165-168(2026)
Published：30 August 2026
DOI：10.47297/wspceWSP2516-251932.20261004
Accepted：

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Zheng Zhi,Chen Guoqing.Artificial Intelligence in Full-Cycle Risk Management for Technology Credit: Mechanisms, Challenges, and Institutional Adaptation[J].Creative Economy,2026,10(04):165-168.
DOI：

Zheng Zhi,Chen Guoqing.Artificial Intelligence in Full-Cycle Risk Management for Technology Credit: Mechanisms, Challenges, and Institutional Adaptation[J].Creative Economy,2026,10(04):165-168. DOI： 10.47297/wspceWSP2516-251932.20261004.

摘要

Abstract

Technology-intensive small and medium-sized enterprises convert research and development into productivity gains

yet their financing remains constrained by intangible assets

volatile cash flows

and information asymmetry that collateral-based risk control cannot absorb. This paper examines how artificial intelligence reconstitutes credit risk management for technology lending as an integrated full-cycle system rather than as isolated algorithmic checkpoints. Drawing on a conceptual analysis of peer-reviewed research

institutional reports

and documented industry practice

the paper diagnoses why the risk structure of technology credit defeats stage-segmented control; traces how machine learning transforms pre-loan screening

in-loan monitoring

and post-loan response; and argues that the decisive gains arise from feedback across stages rather than accuracy at any single checkpoint. Realizing this paradigm is constrained by fragmented data infrastructure

model opacity

concept drift

and thin organizational capability. The paper concludes that full-cycle risk control is an institutional project requiring unified data governance

lifecycle model risk management

and deliberately designed human–machine collaboration.

关键词

Keywords

references

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