AI-Driven Automation and Job Displacement in Credit Risk Functions: Examining the Transformation of Credit Analyst Roles in India’s FinTech Lending Sector

Purpose: The rapid diffusion of artificial intelligence (AI)-enabled automation in credit scoring, underwriting, and risk-monitoring systems is reshaping the credit risk function within India's FinTech lending sector. This study examines how credit analysts' perceptions of AI automation capability influence perceived job insecurity, skill obsolescence, reskilling intentions, perceived job displacement, and career adaptability. Design/Methodology/Approach: Drawing on task-based automation theory, the Job Demands-Resources (JD-R) framework, and technology-acceptance literature, an eight-construct research model with associated hypotheses is developed. Data from credit analysts, underwriters, and credit risk managers employed across FinTech non-banking financial companies (NBFCs), FinTech-bank joint ventures, and digital lending platforms in India are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS. Findings: The measurement model demonstrates satisfactory reliability and convergent validity (Cronbach's alpha and composite reliability above 0.83; average variance extracted above 0.65 for all constructs), and discriminant validity is established via the heterotrait-monotrait (HTMT) ratio. Structural results indicate that perceived AI automation capability significantly predicts both skill obsolescence perception and job insecurity, which jointly predict perceived job displacement; organizational change readiness and reskilling intention act as adaptive mechanisms that partially offset displacement-related outcomes and support career adaptability. Originality/Value: This study contributes one of the first theory-driven, quantitatively validated models of AI-induced role transformation specific to the credit risk function in an emerging-market FinTech context, offering a replicable measurement instrument and actionable guidance for human resource and risk management practice.