Risk Control of Online Lending Platform Under Regulatory Penalty

The existence of information asymmetry will make the online lending platform have the motivation to take actions that harm the interests of investors, and the limited liability of the platform will further aggravate such behaviors, which eventually leads to the occurrence of moral hazard platform. Based on this, this paper designs an incentive method to punish overdue amounts that exceeds the given threshold and analyzes the impact of this method on alleviating the moral hazard problem. We find that when the performance‐flow sensitivity of investors exceeds a certain threshold, the platform will be motivated to choose a high‐effort level regardless of the strictness of regulatory requirements; conversely, whether the effort level selected by the platform meets the regulatory requirements is related to the stability preference weight of the regulator. When the platform fails to meet regulatory requirements, setting appropriate penalty level can incentive the platform to increase its effort level and comply with the regulatory requirements. In addition, we obtain the optimal punishment levels under different stability preference weights of regulator.

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Publication Details

Journal
Discrete Dynamics in Nature and Society
Published
2025-01-01
DOI
https://doi.org/10.1155/ddns/3289853
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00

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article

Risk Control of Online Lending Platform Under Regulatory Penalty

Yaling Chen, Chen Xiao
Discrete Dynamics in Nature and Society
FinTech, Crowdfunding, Digital Finance
article

Risk Control of Online Lending Platform Under Regulatory Penalty

Yaling Chen, Chen Xiao
article en

Abstract

The existence of information asymmetry will make the online lending platform have the motivation to take actions that harm the interests of investors, and the limited liability of the platform will further aggravate such behaviors, which eventually leads to the occurrence of moral hazard platform. Based on this, this paper designs an incentive method to punish overdue amounts that exceeds the given threshold and analyzes the impact of this method on alleviating the moral hazard problem. We find that when the performance‐flow sensitivity of investors exceeds a certain threshold, the platform will be motivated to choose a high‐effort level regardless of the strictness of regulatory requirements; conversely, whether the effort level selected by the platform meets the regulatory requirements is related to the stability preference weight of the regulator. When the platform fails to meet regulatory requirements, setting appropriate penalty level can incentive the platform to increase its effort level and comply with the regulatory requirements. In addition, we obtain the optimal punishment levels under different stability preference weights of regulator.

Discrete Dynamics in Nature and SocietyVol. 2025(1)
Hunan University of Traditional Chinese Medicine (CN), Xinyu University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hunan Province
Openalex Percentile: Top 50%
FinTech, Crowdfunding, Digital Finance
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