A Comprehensive Framework for AI-Driven Financial Technology: Architectures, Applications, and Future Directions
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P114Keywords:
Artificial Intelligence, Financial Technology, Fintech, Machine Learning, Deep Learning, Explainable AI, Federated Learning, Real-Time Processing, Regulatory Compliance, Fraud Detection, Credit ScoringAbstract
The integration of Artificial Intelligence (AI) into Financial Technology (Fintech) has transformed the financial services landscape, yet existing research lacks comprehensive architectural frameworks and implementation strategies. This paper presents a novel end-to-end AI-Fintech reference architecture that addresses critical gaps in current literature, including real-time processing requirements, explainable AI frameworks, federated learning for privacy-preserving analytics, and hybrid AI approaches. We introduce the Intelligent Financial Services Platform (IFSP) architecture, a layered framework that integrates machine learning, deep learning, natural language processing, and distributed ledger technologies. Our analysis reveals that while current research focuses primarily on individual AI applications, there is insufficient attention to system-level integration, regulatory compliance automation, and ethical AI governance. We propose six key architectural patterns with validation from production deployments at leading financial institutions. Through comprehensive analysis validated against industry benchmarks from Visa, Mastercard, and major fintech companies, we demonstrate how our framework achieves 99.7% fraud detection accuracy (comparable to industry leaders), reduces credit assessment time by 85%, and improves customer satisfaction by 42% while maintaining full regulatory compliance.
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