Integrated AI Impact Measurement Framework for FinTech: From Outcome Definition to Continuous Monitoring (Published)
This article presents a comprehensive framework for measuring the impact of artificial intelligence investments in financial technology organizations. The architecture establishes a results-oriented approach constructed around five integrated elements: business-aligned outcome specification, structured data acquisition protocols, causal determination analytical methodologies, formalized return-on-investment evaluation procedures, and ongoing performance surveillance systems. Through detailed case studies of AI implementation in customer service optimization and fraud detection, the article demonstrates how proper measurement methodologies significantly enhance return on investment. The article extends to an evaluation of open-source tools for AI ROI assessment, examining their features, selection criteria, and integration considerations. The framework addresses a critical gap between AI implementation enthusiasm and impact verification, enabling FinTech organizations to make more informed investment decisions, optimize AI strategies based on empirical evidence, and effectively communicate value creation to stakeholders. Future trends reveal promising research directions including alternative data integration, temporal modeling advancements, and explainable AI development, alongside persistent challenges in uncertainty quantification and cross-organizational standardization.
Keywords: FinTech measurement framework, artificial intelligence ROI, explainable AI decision-making, open-source financial analytics, outcomes-first approach