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This is a Human Clinical Trial study (also classified as: Observational).

Leveraging Kafka for Event-Driven Architecture in Fintech Applications

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The LK-EDA-FA-BSCNN method achieves 98.5% accuracy in predicting future transactions.
Performance metrics show 95.3% precision and a runtime of 1.150 seconds, ensuring efficient processing.
Adaptive two-stage unscented kalman filter cleans input data, crucial for downstream analysis success.
The architecture incorporates kafka streams, highlighting its importance in reliable fintech applications.

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Proposed method improves payment processing in fintech, achieving 98.5% accuracy using kafka and machine learning.

Modadugu et al. (2025) studied this question.

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