Paper: arXiv 2411.17783

Authors: Kun Liu, Jin Zhao

Abstract

In the field of finance, the prediction of individual credit default is of vital importance. However, existing methods face problems such as insufficient interpretability and transparency as well as limited performance when dealing with high-dimensional and nonlinear data. To address these issues, this paper introduces a method based on Kolmogorov-Arnold Networks (KANs). KANs is a new type of neural network architecture with learnable activation functions and no linear weights, which has potential advantages in handling complex multi-dimensional data. Specifically, this paper applies KANs to the field of individual credit risk prediction for the first time and constructs the Kolmogorov-Arnold Credit Default Predict (KACDP) model. Experiments show that the KACDP model outperforms mainstream credit default prediction models in performance metrics (ROC_AUC and F1 values). Meanwhile, through methods such as feature attribution scores and visualization of the model structure, the model’s decision-making process and the importance of different features are clearly demonstrated, providing transparent and interpretable decision-making basis for financial institutions and meeting the industry’s strict requirements for model interpretability. In conclusion, the KACDP model constructed in this paper exhibits excellent predictive performance and satisfactory interpretability in individual credit risk prediction, providing an effective way to address the limitations of existing methods and offering a new and practical credit risk prediction tool for financial institutions.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel application of Kolmogorov-Arnold Networks (KANs) to credit default prediction, combining the underlying mathematical representation theorem with empirical model evaluation against standard baselines. While the methodology introduces an interesting neural architecture for financial tabular data, the empirical validation details in the provided excerpt remain fairly standard.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve interpretability & performance in credit default prediction] --> B[Data Input: High-dimensional & nonlinear credit data]
    B --> C[Methodology: Apply Kolmogorov-Arnold Networks to construct KACDP model]
    C --> D[Model Training: Utilize learnable activation functions with no linear weights]
    D --> E[Interpretability Analysis: Calculate feature attribution scores & visualize network structure]
    E --> F[Outcomes: Superior performance ROC_AUC & F1 and transparent decision-making]

Cited References (42)

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0 of 42 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.