AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency

Qi Shen

doi:10.54254/2755-2721/69/20241494

Research Article

Open access

Published on 25 July 2024

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Shen,Q. (2024). AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency. Applied and Computational Engineering,69,134-139.

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AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency

Qi Shen *^,1,

¹ Singapore management university, Singapore

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/69/20241494

Abstract

The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.

Keywords

AI, Financial Risk Management, Predictive Modeling, Real-Time Data Analysis

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References

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Cite this article

Shen,Q. (2024). AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency. Applied and Computational Engineering,69,134-139.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/

ISBN：978-1-83558-459-0(Print) / 978-1-83558-460-6(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.69

ISSN：2755-2721(Print) / 2755-273X(Online)

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