Algorithms Co-Motivation or Consumer Welfare? - A Study on the Double-Edged Sword Effect and Mechanism of Generative AI in Precision Marketing in Financial Technology

The question that this paper aims to address is whether firm incentive is the primary driver of generative AI in FinTech precision marketing, or whether consumer welfare is the primary driver, and how the same algorithms can generate consumer benefit and consumer harm. Theoretical framing employs a double-edged effect approach, and this implies that there are both positives and negative outcomes of the same personalization processes. The research solely employs secondary data, as it combines anonymized FinTech marketing logs, natural-experiment rollout logs, and open-access controlled experiment repositories to mediate by personalization and moderate by transparency. The methods of analysis are descriptive statistics, difference-in-means, linear probability model, difference-in-differences, heterogeneity test and mediation analysis. The initial anticipations are that AI-personalized messages increase aggregate conversion and engagement, however, more significantly they increase subgroup differences and privacy or complaint indicators where there is low transparency which is synonymous with existing findings on usefulness, bias, and cyber risks. To inform policy, the paper proposes governance measures such as transparency labels, fairness audits, and mandatory reporting of parity gaps, practical steps to balance innovation and consumer protection.