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

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 challengesincluding model interpretability, regulatory transparency, data bias, and privacy concernsare 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.