A systematic review of artificial intelligence, machine learning, and environment–social–governance in marketplace lending

Abstract The convergence of artificial intelligence (AI), machine learning (ML), and environmental, social, and governance (ESG) considerations has transformed financial decision-making. This transformation yields several advantages, including enhanced predictive accuracy, real-time fraud detection, expanded access for underserved populations, integration of sustainability metrics into credit models, and increased transparency and regulatory compliance. This systematic review addresses four research questions using the Antecedents-Decisions-Outcomes (ADO) framework to map, synthesize, and critically evaluate the AI/ML-ESG nexus within marketplace lending. Following PRISMA-2020 guidelines, 555 peer-reviewed studies published between January 2015 and December 2025 were identified from Scopus and Web of Science and analyzed through narrative synthesis. Methodological trends have shifted from statistical approaches (65% through 2017) to machine learning (2018–2021), deep learning (2020–2023), and, most recently, explainable AI with ESG integration (42% of studies published from 2024 onward). Based on descriptive comparison of individually reported results across heter