Temporal-Aligned Meta-Learning for Risk Management: A Stacking Approach for Multi-Source Credit Scoring

ArXiv ID: 2601.07588 “View on arXiv”

Authors: O. Didkovskyi, A. Vidali, N. Jean, G. Le Pera

Abstract

This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credit scoring models. The approach aligns financial statement reference dates with evaluation dates, mitigating bias arising from publication delays and asynchronous data sources. It is based on a two-step temporal decomposition that at first estimates annual probabilities of default (PDs) anchored to balance-sheet reference dates (December 31st) through a static model. Then it models the monthly evolution of PDs using higher-frequency behavioral data. Finally, we employ stacking-based architecture to aggregate multiple scoring systems, each capturing complementary aspects of default risk, into a unified predictive model. In this way, first level model outputs are treated as learned representations that encode non-linear relationships in financial and behavioral indicators, allowing integration of new expert-based features without retraining base models. This design provides a coherent and interpretable solution to challenges typical of low-default environments, including heterogeneous default definitions and reporting delays. Empirical validation shows that the framework effectively captures credit risk evolution over time, improving temporal consistency and predictive stability relative to standard ensemble methods.

Keywords: Credit Risk Assessment, Small and Medium Enterprises (SMEs), Probability of Default (PD), Meta-Learning, Temporal Decomposition

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs relatively straightforward logistic regression and EWMA for temporal alignment, resulting in low math complexity, but it is backed by a substantial dataset with specific time periods, frequency details, and validation metrics like McFadden R2 and KS, indicating high empirical rigor.

  flowchart TD
    A["Research Goal<br>Develop a meta-learning framework<br>for temporal alignment in SME credit risk assessment"] --> B

    subgraph B ["Methodology"]
        B1["Step 1: Temporal Decomposition<br>Static model estimates annual PDs<br>anchored to balance-sheet dates"] --> B2
        B2["Step 2: Monthly Evolution Modeling<br>Uses high-frequency behavioral data<br>to model PD dynamics"] --> B3
        B3["Step 3: Stacking Architecture<br>Aggregates base models into a unified<br>predictive ensemble"]
    end

    B --> C["Data & Inputs<br>Italian SME Financial Statements<br>Behavioral Transaction Data<br>Default Events"]

    C --> D["Computational Processes<br>1. Static Model Training<br>2. Temporal Alignment & Evolution Modeling<br>3. Meta-Learner (Stacking) Training"]

    D --> E["Key Outcomes & Findings"]
    subgraph E ["Results"]
        E1["Improved Temporal Consistency<br>Addresses publication delays and<br>asynchronous data sources"]
        E2["Enhanced Predictive Stability<br>Superior performance vs. standard<br>ensemble methods"]
        E3["Interpretable Framework<br>Coherent solution for low-default<br>environments"]
    end