Revolutionizing FinTech with Event-Driven Architectures - Abhinav Reddy Jutur, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
This article serves as a comprehensive guide for financial technology architects, FinTech executives, and software engineers seeking to leverage Event-Driven Architectures (EDAs) in modern financial systems. It begins by examining the theoretical framework of EDAs and their advantages over traditional architectures in financial systems, providing technical decision-makers with clear implementation rationales. The article then delves into the key technologies enabling EDA implementation in FinTech, including Apache Kafka, Apache Pulsar, and cloud-native event buses, offering software architects practical insights for technology selection. Real-world applications of EDAs are discussed, with a focus on real-time fraud detection and dynamic credit scoring, demonstrating concrete business value for FinTech leaders. The article also highlights cutting-edge innovations in high-frequency trading, personalized financial recommendations, and regulatory compliance made possible by EDAs. Looking to the future, the synergies between EDAs and artificial intelligence are explored, particularly in the realm of predictive analytics for risk assessment, market forecasting, and customer behavior analysis – crucial knowledge for AI/ML engineers working in financial services. Finally, the article addresses the challenges and considerations in implementing EDAs, including data privacy and security, scalability and performance issues, and integration with legacy systems, providing technology leaders with a roadmap for successful implementation. This comprehensive analysis equips technical decision-makers and practitioners with valuable insights into how EDAs are reshaping the FinTech landscape and paving the way for more responsive, efficient, and innovative financial services.
Keywords: Event-Driven Architecture; FinTech Innovation; Real-time Processing; Predictive Analytics; Financial Data Streaming