Experimental Methods and A/B Testing in FinTech: Applied Guide in Python

Abstract

This paper provides an applied guide to the use of experimental methods, particularly A/B testing, in the FinTech sector, leveraging Python-based tools. It examines how these techniques validate business hypotheses, optimize interfaces, personalize financial services, and enhance critical metrics such as conversion and retention. Using a rigorous experimental design, the study evaluates treatment effects on conversion rates, deposits, and user activity through t tests, z tests, bootstrap resampling, and regression models. Results confirm a significant uplift in conversion rates but show no substantial effects on deposits or post-conversion engagement, thereby delimiting the scope of impact. Heterogeneous effects across age segments are identified, with strategic implications for targeted personalization and risk management. The article highlights the importance of incorporating ethical safeguards, transparency, and regulatory compliance when experimenting with sensitive financial data, and proposes a replicable, scalable methodological framework for researchers and practitioners.