ABSTRACT
The global rise of automated debt collection practices has created an urgent need to reimagine public enforcement systems. While private actors increasingly deploy digital tools to streamline debt collection, public enforcement mechanisms remain largely analog, creating inefficiencies and procedural barriers that harm both creditors and debtors. This Article proposes a novel framework for modernizing public debt enforcement through the strategic integration of blockchain-based smart contracts and artificial intelligence. Drawing on comparative analysis, with particular attention to unique enforcement system in specific civil law countries and the EU, this Article demonstrates how automated protocols could transform debt enforcement while preserving due process protections. The Article makes three key contributions. First, it develops a theoretical model for embedding smart contract automation within public enforcement systems, showing how ‘if/then’ protocols can reduce procedural delays while enhancing transparency. Second, it analyzes how machine learning algorithms can support human decision-making in enforcement proceedings, particularly in areas requiring context-sensitive judgment. Third, it addresses the critical governance challenges raised by automated enforcement, proposing specific oversight mechanisms to ensure accountability and fairness. This analysis yields important insights for policymakers and scholars grappling with questions of automation in law more broadly. The Article concludes by offering concrete recommendations for implementing these technological solutions in ways that enhance efficiency while protecting fundamental rights in the enforcement process.
Koksal, Bahadir, Digitalization of Debt Enforcement Systems: Smart Contracts or AI Solutions? (December 24, 2024).
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