arXiv:2605.29478v1 Announce Type: cross Abstract: Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerability to financial distress. Predicting SME default is therefore crucial for financial institutions, policymakers, and researchers. Recent advances in machine learning (ML) have improved predictive performance in credit risk modeling. Yet, the limited interpretability of complex models raises concerns regarding transparency and regulatory compliance. This study investigates SME's default predictors
The increasing adoption of complex machine learning models in financial risk assessment, coupled with growing regulatory scrutiny on algorithmic transparency, makes interpretability a crucial focus.
Improved interpretability of AI models in financial services can unlock greater trust, facilitate regulatory compliance, and allow for more robust risk management, particularly for economically vital SMEs.
The ability to extract understandable rules from complex ML models shifts the paradigm from 'black box' predictions to transparent decision-making in credit risk, potentially accelerating AI adoption in regulated industries.
- · Financial Institutions (AI adopters)
- · SMEs (access to credit)
- · AI/ML explainability platforms
- · Regulators
- · Traditional credit rating agencies (if slow to adapt)
- · Opaque AI model providers
- · Companies relying on non-interpretable models
Financial institutions can deploy AI models for SME default prediction with greater confidence and regulatory approval.
Increased transparency and trust in AI-driven credit decisions could lead to more efficient capital allocation and reduced borrowing costs for SMEs.
The methodology could be extended to other regulated fields, driving a broader demand for 'explainable AI' across industries and potentially influencing future AI regulatory frameworks.
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Read at arXiv cs.AI