Strengthening Consumer Protection and Growth Investment Management: Application of Data-driven Framework in Fintech

Abstract

In the fintech landscape, rapid platform expansion has amplified consumer exposure to concentrated financial risks while widening gaps in risk compensation and protection. To address these challenges, this study proposes and empirically evaluates a unified data-driven framework that integrates customer profiling, behavioral monitoring, and complaint mining to enhance targeted consumer protection. The framework further incorporates multi-source data into goal-oriented asset allocation, scenario-based investment advisory, and sentiment-aware risk management to improve medium- and long-term investment outcomes. Using a 12-month pre-post observation of a large-scale internet wealth management platform, the empirical analysis examines changes in consumer risk exposure, complaint incidence, transaction risk control, portfolio drawdown, target achievement rates, and customer retention. Results show that the proportion of high-risk products held in protected accounts and complaint rates declined substantially following implementation, while abnormal transaction interception rates improved. At the portfolio level, maximum drawdowns of education and pension target portfolios narrowed, and the probability of achieving predefined return objectives increased. Overall, these findings have broader implications for digital financial governance and consumer risk mitigation in advanced fintech markets, including the United States.