COMPARATIVE LEGAL APPROACHES TO ARTIFICIAL INTELLIGENCE REGULATION IN PEER-TO-PEER LENDING

Abstract

The rapid integration of artificial intelligence (AI) in peer-to-peer (P2P) lending platforms has transformed credit assessment, risk management, and service efficiency within the financial technology sector. However, the extensive use of AI also raises significant legal concerns, particularly related to transparency, accountability, consumer protection, and potential algorithmic bias. This paper examines and compares legal approaches to regulating artificial intelligence in P2P lending across selected jurisdictions. Using a normative legal research methodology and comparative legal approach, the study analyzes regulatory frameworks, policy instruments, and governance principles governing AI deployment in P2P lending services. The comparison highlights differences in regulatory emphasis, ranging from innovation-driven approaches to rights-based and risk-based regulatory models. The findings indicate that jurisdictions with comprehensive AI governance frameworks tend to provide stronger safeguards for consumers while maintaining support for financial innovation. Conversely, fragmented or sector-specific regulations often create regulatory gaps that may undermine legal certainty and financial inclusion objectives. This paper argues that an effective AI regulatory approach in P2P lending should balance technological innovation with legal accountability and inclusive finance principles. The study contributes to the discourse on fintech regulation by offering comparative insights that may inform policymakers in developing adaptive and inclusive legal frameworks for AI-driven financial services.