Artificial Intelligence and Customer Acquisition in Retail Financial Services: Experimental Evidence from Insurance Distribution

Selection markets create a multitasking environment where intermediary agents often need to increase consumer take-up as well as resolve information asymmetries about consumer expected cost during the sales process. I study how artificial intelligence (AI) affects attention allocation and information production in human-intermediated markets by analyzing a large-scale randomized experiment conducted by a top insurance agency in China. In the experiment, the firm provided treated agents with an AI-generated estimation of consumer demand for insurance, based on consumer digital footprints on the advertisements on social media; these footprints were available to all agents prior to the experiment. I show that AI demand prediction shifts agents’ attention to converting high-intent consumers, improving agents’ sales by 14%. As an unintended consequence, AI-generated demand information reduces agents’ own information acquisition and increases adverse selection, consistent with attention models and a crowding out of risk information. Moreover, treated agents bring in riskier consumers but do not match them to more expensive products to achieve stronger incentive compatibility. The findings suggest that a common application of AI to predict consumer demand can have side effects on human information production, market efficiency, and can exacerbate agency conflicts when intermediary agents maximize their own surplus from AI