Can artificial intelligence enhance service quality?: Evidence from US financial services

Purpose Service quality is pivotal to investigating cognitive and affective engagement across a customer journey (e.g. pre-service, service, and post-service) that leads to customer satisfaction and loyalty. This research examines how algorithm-assisted AI financial services change service quality by analyzing consumer narratives. This approach, grounded in narrative theory, helps explain a customer’s subjective and objective responses to direct and indirect service interactions throughout the customer journey. Design/methodology/approach Consumer complaints filed with the U.S. Consumer Financial Protection Bureau (2015–2024) were retrieved for Structural Topic Modeling, an unsupervised machine learning technique for Natural Language Processing. The critical incidents in AI financial services were analyzed to understand service quality as consumer feedback, revealing consumer-centric service evaluations of various AI financial services across front- and back-office setups. Findings The results identified topics and topic clusters that show consumer responses to broken promises in service quality dimensions (e.g. coherence, personalization and seamlessness). Compliance with regulations (e.g. the Fair Credit Reporting Act and the Equal Credit Opportunity Act) throughout the customer journey affects service quality in AI financial services, as ethical AI practices matter for service quality. Originality/value This article proposes how machine learning can analyze service quality as consumer feedback to support ethical AI practices in financial services, offering theoretical and practical implications. Repeated AI financial service failures (e.g. the frame problem) occur due to the mindless operation of algorithm-assisted automation. Application of machine learning on service quality allows us to investigate how actors define their ideal service quality throughout a customer journey, advancing our understanding of consumers’ goals and unfolding the customer–AI dynamics.