Transforming fintech product strategies through AI-augmented machine learning optimization and continuous six sigma feedback loops

Foluke Ekundayo 1, * and Chioma Onyinye Ikeh 2

1 University of Maryland Global Campus USA.
2 Product Development and Strategic Marketing, UK.
 
Review
International Journal of Science and Research Archive, 2021, 02(01), 259-277.
Article DOI: 10.30574/ijsra.2021.2.1.0004
Publication history: 
Received on 04 January 2021; revised on 22 March 2021; accepted on 29 March 2021
 
Abstract: 
The rapid evolution of financial technology (fintech) has intensified the need for adaptive, data-informed product strategies capable of responding to volatile market conditions, user demands, and regulatory shifts. Traditional product development models—characterized by linear planning and siloed decision-making—have proven insufficient in addressing the complexity and velocity of modern fintech ecosystems. This paper introduces an AI-augmented framework that integrates machine learning (ML) optimization with continuous Six Sigma feedback loops to enhance product roadmap planning, execution, and quality assurance in fintech environments. At the core of this approach is the use of supervised learning models, particularly Support Vector Machines (SVM), to classify and prioritize product features based on real-time inputs from user behavior analytics, defect logs, compliance flags, and market feedback. These predictive insights are seamlessly embedded into Agile sprints and design cycles, ensuring each iteration aligns with business value and quality metrics. Complementing the ML layer, the framework employs Six Sigma principles to monitor defects per million opportunities (DPMO), root cause indicators, and control metrics that support continuous improvement and accountability. This hybrid model enables fintech firms to adopt a proactive product development posture—one that is simultaneously data-driven, user-centric, and risk-conscious. The system also supports traceability and governance by integrating explainable AI components and real-time visualization dashboards. Empirical tests demonstrate improved prioritization accuracy, reduced defect rates, and enhanced stakeholder alignment. By bridging predictive intelligence with disciplined quality frameworks, this research offers a scalable, adaptable solution for modern fintech organizations seeking to optimize product outcomes through automation, collaboration, and continuous learning.
 
Keywords: 
AI-Augmented Product Strategy; Fintech Innovation; Support Vector Machine; Six Sigma; Agile Development; Machine Learning Optimization
 
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