Algorithmic transformation of financial risk identification methods: from traditional models to data-intelligent frameworks

Shuai Yuan

doi:10.54254/2977-5701/2025.25760

Journal of Applied Economics and Policy StudiesOpen access

Algorithmic transformation of financial risk identification methods: from traditional models to data-intelligent frameworks

Research Article

Open Access

Algorithmic transformation of financial risk identification methods: from traditional models to data-intelligent frameworks

Shuai Yuan ^1*

¹ China Securities Co., Ltd., Beijing, China

^*Corresponding author: 395001740@qq.com

Published on 11 August 2025

JAEPS Vol.18 Issue 7

ISSN (Print): 2977-571X

ISSN (Online): 2977-5701

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Abstract

This paper explores the algorithmic transformation of financial risk identification methods, tracing the shift from traditional statistical approaches to advanced data-intelligent frameworks. Traditional models such as logistic regression and expert systems, while foundational, often struggle with nonlinear relationships, high-dimensional data, as well as real-time responsiveness and adaptive capacity. In contrast, a novel multimodal framework integrating graph neural networks (GNN) and temporal deep learning (LSTM) based on machine learning, deep learning, and graph-based models offers superior predictive accuracy, adaptability, and scalability. The study examines the application of these algorithms in credit risk assessment, fraud detection, and systemic risk forecasting, while also integrating quantitative tools such as dynamic VaR, Monte Carlo simulations and performance metrics like AUC and F1-score. Key challenges—including model interpretability, regulatory transparency, data bias, and privacy concerns—are assessed and mitigated by Shapley-value-based XAI and federated learning techniques. The paper concludes by outlining future directions such as explainable AI(XAI), causal inference, AutoML, and multimodal data integration toward real-time resilient risk governance systems. These innovations signal a move toward more intelligent, transparent, and resilient financial risk management systems.

Keywords:

financial risk identification, machine learning, deep learning, explainable AI(XAI), quantitative analysis

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References

[1]. Bi, S., Lian, Y., & Wang, Z. (2023). Research and design of a financial intelligent risk control platform based onbig data analysisand deep machine learning.In International Conference on Big Data Analytics for Cyber-Physical System in Smart City(pp. 395–404). Singapore: Springer Nature Singapore.

[2]. Peng, Y., Wang, G., Kou, G., & Shi, Y. (2011). An empirical study of classification algorithm evaluation for financial risk prediction.Applied Soft Computing, 11(2), 2906–2915.

[3]. Javaid, H. A. (2024). Ai-driven predictive analytics in finance: Transforming risk assessment and decision-making.Advances in Computer Sciences, 7(1).

[4]. Xu, H., & Yang, L. (2024). Enhancing Financial Risk Prediction Through Echo State Networks and Differential Evolutionary Algorithms in the Digital Era.Journal of the Knowledge Economy, 1–22.

[5]. Han, M. (2024). Systematic financial risk detection based on DTW dynamic algorithm and sensor network.Measurement: Sensors, 34, 101257.

[6]. Gao, Y. (2024). Design and implementation of financial risk control system based on machine learning algorithm andbig data technology.In Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024)(Vol. 13269, pp. 149–157). SPIE.

References

[2]. Peng, Y., Wang, G., Kou, G., & Shi, Y. (2011). An empirical study of classification algorithm evaluation for financial risk prediction.Applied Soft Computing, 11(2), 2906–2915.

[3]. Javaid, H. A. (2024). Ai-driven predictive analytics in finance: Transforming risk assessment and decision-making.Advances in Computer Sciences, 7(1).

[4]. Xu, H., & Yang, L. (2024). Enhancing Financial Risk Prediction Through Echo State Networks and Differential Evolutionary Algorithms in the Digital Era.Journal of the Knowledge Economy, 1–22.

[5]. Han, M. (2024). Systematic financial risk detection based on DTW dynamic algorithm and sensor network.Measurement: Sensors, 34, 101257.

Cite this article

Yuan,S. (2025). Algorithmic transformation of financial risk identification methods: from traditional models to data-intelligent frameworks. Journal of Applied Economics and Policy Studies,18(7),62-66.

Data availability

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

About volume

Journal: Journal of Applied Economics and Policy Studies

Volume number: Vol.18

Issue number: Issue 7

ISSN: 2977-5701(Print) / 2977-571X(Online)

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