Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment

Kangming Xu; Biswajit Purkayastha

doi:10.5281/zenodo.14077150

OPEN ACCESS |Research Article ||16 November 2024

Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment

Kangming Xu

Santa Clara University

etekedahibi@outlook.com

Computer Science and Engineering , Santa Clara University, CA, USA.

Biswajit Purkayastha

Cihan University Sulaimaniya

Department of Computer Science, Cihan University Sulaimaniya, Sulaimaniya, Iraq.

* Corresponding Author¹: Kangming Xu, E-Mail: etekedahibi@outlook.com

* Corresponding Author²: Biswajit Purkayastha, E-Mail:

Publication

Accepted Unknow ; Published 2024 November 16

Academic Journal of Sociology and Management, 2024, 2(6), 3005-5040.

Abstract

With the continuous development of artificial intelligence and various new intelligent algorithm technologies, the business contacts between various institutions within financial enterprises are gradually increasing, and traditional financial risk management can no longer adapt to the current status quo in the era of big data. The lack of information sharing among institutions can reduce the efficiency of financial management and adversely affect the operation of enterprises. At present, financial credit risk mainly includes credit risk, market risk and operational risk. Credit risk relates to the possibility that a borrower will not be able to repay loans or debts on time, market risk covers potential losses caused by market volatility, price changes and adverse events, while operational risk includes risks such as internal operational errors, technical failures and fraud, which may adversely affect the normal operations and financial condition of a financial institution. These risk factors need to be integrated and managed in the financial sector to ensure financial stability and customer trust. Therefore, this paper aims to establish a KMV financial credit risk model, continuously strengthen the internal risk management of enterprises, achieve management modeling and a good KMV algorithm mechanism, and realize the cooperation and stickiness between customers and enterprises, so as to avoid unnecessary financial risks.

Keywords

Risk Management , Intelligent Algorithm , Financial Credit Risk , KMV Model Building , Cloud Computing .

Metadata

DOI:

10.5281/zenodo.14077150

ARK:

ark:/40704/AJSM.v2n6a04

Pages: 19-24

References: 20

Disciplines: Finance

Subjects: Risk Management

Cite This Article

APA Style

Xu, K. & Purkayastha, B. (2024). Integrating artificial intelligence with kmv models for comprehensive credit risk assessment. Academic Journal of Sociology and Management, 2(6), 19-24. https://doi.org/10.5281/zenodo.14077150

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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