Citation :

Niloofar Ebrahimi, Mina Ghasemi, Mitra Shiri, 2026. "Event-Stream Intelligence for Low-Latency Transaction Processing in Enterprise Financial Systems" ESP International Journal of Science, Humanities & Management Studies(ESP-IJSHMS)  Volume 4, Issue 1: 18-27.

Abstract :

Enterprise financial systems face tremendous transaction processing architectures since those have to react instantly while constantly changing business events without sacrificing accuracy, security, scalability and compliance with the regulations. Most financial institutions rely on traditional batch-oriented processing models which result in high latency and delay the ability to detect fraud, evaluate transactions, update customer information or respond promptly to market conditions. Instead of waiting for batches to be consolidated and processed, event-stream intelligence does continuous processing of transaction events as they emerge from different sources — using analytical, rule-based, or machine-learning methods that help you make low-latency decisions. Abstract This study investigates the use of event stream intelligence for transaction processing in large-scale enterprise financial systems.
The study examines iv stream ingestion ii event processing, iii real-time analytics, iv intelligent anomaly detection v distributed architectures vi state management vii fault tolerance and security mechanisms. It also examines how event brokers, stream-processing engines, in-memory computing, complex event processing and machine-learning models can improve transactional latency without sacrificing consistency or reliability. In particular, the research suggests an architecture that continuously captures transaction events and enriches them for further evaluation/routing based on business as well as risk needs. Special care is taken to balance speed of processing against other relevant factors, such as accurate financial presentations, audit ability and compliance with privacy standards or legal requirements. Event-stream intelligence brings new levels of responsiveness, fraud detection and operational visibility plus facilitates scalability for any number of transactions when supported by resilient distributed architectures and carefully governed processing policies according to the findings.

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Keywords :

Event-Stream Intelligence, Low Latency Processing, Financial Systems, Transaction Processing Related Articles Stream Awareness in Distributed Computing via Flexible Time Constraints Real-time Analytics of Temporal Patterns Through Data Streams Error-Tolerant In-Memory Events Recognition Within Time Windows Efficient High Layer Traffic Flow Analysis for Network Security In the era when Big data is proliferating from everywhere and every programmable device through Internet to more seconds than we can manage — many Fifteen-year-old niched-data platforms have evolved into Firehouse systems which accommodate incoming event streams continuously.