Purpose: This paper investigates the architectural and analytical benefits of leveraging Apache Kafka for implementing Event Sourcing principles to achieve ultra-low latency, real-time risk profiling in complex financial institutions. The study addresses a critical gap in the literature concerning the architectural underpinnings required for capturing event causality and immutability, which are fundamental to modern compliance and risk attribution.

Design/Methodology/Approach: A high-throughput, distributed Event Sourcing architecture was designed and implemented using Kafka as the immutable log and stream-processing engine. The Real-Time Risk Profiling Model (RTRP-M) was developed on Kafka Streams to perform continuous, stateful aggregation of simulated high-volume financial events. Performance metrics focused on end-to-end latency, throughput under stress, and State Reconstruction Time (S-RT).

Findings: The implemented architecture demonstrates sustained ingestion throughput exceeding $10^5$ events per second, with a mean end-to-end latency for risk metric calculation of less than 10 milliseconds. Notably, the system exhibited instantaneous S-RT capabilities, enabling the reconstruction of entity state at any historical moment, which is critical for back-testing and audit trails. The findings are associated with significantly enhanced capabilities for real-time anomaly detection and risk attribution compared to traditional batch-oriented systems.

Originality/Value: This work systematically connects the architectural paradigm of Event Sourcing with the analytical requirements of complex, adaptive risk management. It provides a blueprint for financial technology practitioners seeking to transition to a true real-time operational posture, demonstrating how the fundamental properties of the distributed log are paramount to achieving both compliance and competitive advantage.

Event Sourcing, Apache Kafka, Real-Time Risk Analysis

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