Role of artificial intelligence in financial leasing: Assessing bias and real-time value

Small and medium-sized manufacturers often struggle with traditional loan markets to upgrade critical equipment for manufacturing transformation. Financial leasing provides an alternative solution by relying on project profitability rather than borrower's creditworthiness. To examine the impact of artificial intelligence (AI) on addressing delays in loan assessment and mitigating moral hazard due to the separation of ownership and control rights, we develop a game-theoretical framework that models a pull supply chain with a capital-constrained manufacturer who leases equipment from a lessor, and a retailer who finances production through advance payment. The lessor leverages AI to design risk premiums and monitor equipment performance throughout the leasing cycle. Our findings show that AI-enabled improvement of assessment efficiency and relaxion of timeframe encourage the manufacturer to produce more, while moral hazard elimination may reduce the manufacturer's production quantity. However, when AI explainability or inherent quality is high, both the manufacturer and retailer benefit from the reduced financial and agent frictions. Moreover, there exists an optimal AI explainability level at which the manufacturer and retailer secure higher profits, and the lessor minimizes the default risk, achieving a triple-win situation. Our findings suggest that financiers can leverage AI to reduce financial and operational risks in supply chains even when AI is biased, provided it offers clear explanations. However, the biased AI with insufficient explanations should be excluded, as it may exacerbate financial frictions and increase default risks.