Financial institutions continuously generate high-dimensional, heterogeneous and time-series log data in various stages of transaction matching, risk control decision-making, clearing and reconciliation, as well as infrastructure operation and maintenance. Traditional single-machine log analysis solutions have major deficiencies in high-throughput and low-latency operation, cross-node correlation detection, stability under template drift and label scarcity, and false alarm control. This paper presents a distributed anomaly detection system framework of "collection-parsing-representation-detection-interpretation-backflow" for financial log scenarios. Kafka and Flink are employed for streaming access and state computation. A semantic representation, sequence model and rule constraint fusion mechanism are introduced at the model layer. Feature caching, window aggregation, online threshold calibration and alarm grading are used at the engineering level to improve the real-time performance and interpretability of the system. Based on research results and benchmark data in the field of log anomaly detection over the past three years, a methodological analysis will be conducted, and a system design strategy suitable for the collaborative monitoring of financial business logs, system logs, and security logs will be put forward. Research has shown that a distributed processing architecture and multi-source feature fusion models can simultaneously achieve the goals of high-throughput scalability, high-accuracy detection, and easy-to-maintain deployment; thus, a practical path for anomaly detection technology has been proposed for securities, payment, banking and quantitative trading platforms.

Ziyu Zhang. Research on Design and Implementation of a Distributed Anomaly Detection System for Financial Logs in High-Concurrency Risk Control and Settlement Links. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 85-93. https://doi.org/10.38007/ML.2026.060110.

[1] Huang, J. (2025, August). Research on Multi-Model Fusion Machine Learning Demand Intelligent Forecasting System in Cloud Computing Environment. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-7). IEEE.

[2] Hou, Y. (2026). Research on Heterogeneous Server Upgrade Strategies and Resource Utilization Efficiency Oriented Toward Green Computing Objectives. Advances in Computer and Communication, 7(1).

[3] Huang, J. (2025, September). Performance Evaluation Index System and Engineering Best Practice of Production-Level Time Series Machine Learning System. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 01-07). IEEE.

[4] Hou, Y. (2026). Exploration of Data Center Cost Optimization Pathways Under Multi-generation CPU and GPU Collaborative Architectures. Engineering Advances, 6(1).

[5] Yanchun Wang. (2025) Research on Enhancing ERP System Efficiency through AI in Cross-border Supply Chain Environments. Advances in Computer and Communication, 6(5), 268-273.

[6] Zhang, C., Han, J., Zou, Y., Dong, K., Li, Y., Ding, J., & Han, X. (2024, April). Detecting Anomalies in LiDAR Point Clouds. At WCX SAE World Congress Experience. SAE Technical Paper.

[7] Ding, J. (2025). Exploration of Process Improvement in Automotive Manufacturing Based on Intelligent Production. International Journal of Engineering Advances, 2(2), 17-23.

[8] Wu, Y. (2026). Federated Learning-based Algorithm Design for Privacy Preservation in Cross-domain Data Sharing. Engineering Advances, 6(1).

[9] Sun, J. (2025). Research on Business Data-driven Risk Prediction Methods Based on Machine Learning. Advances in Computer and Communication, 6(4).

[10] Yanchun Wang. (2025) Research on Improving ERP System Efficiency with AI in Cross-border Supply Chain Environments. Advances in Computer and Communication, 6(5), 268-273.

[11] Zhou, Y. (2026). Energy efficiency and sustainability strategies for data centers. European Journal of Engineering and Technologies, 2(1), 46-53.

[12] Lu, Z. (2025). Design and Practice of AI Intelligent Mentor System for DevOps Education. European Journal of Education Science, 1(3), 25-31.

[13] Wu Y. Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization[J]. 2025.

[14] Liu, X., & Yang, D. (2025, March). LLM Data Strategy: Improving Data Availability and Efficiency. In Doctoral Symposium on Computational Intelligence (pp. 425-437). Singapore: Springer Nature Singapore.

[15] Zhang, Q. (2025, October). Application of Reinforcement Learning in Dynamic Advertising Content Generation. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-5). IEEE.

[16] Zhang, Q. (2026). Security Improvement and Application of Identity and Access Management in Saas Platform.

[17] Hou, Y. (2026). Research on Server Performance Stability Assurance Mechanisms during Cross-Generation Computing Platform Upgrades.

[18] Wu, Y. (2025, October). Multi-Level Belief Rule Base Modeling Architecture and Intelligent Optimization Technology for Decision Support Systems. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-8). IEEE.

[19] Chen, M. (2026). Research on Privacy-Preserving AI Model Training and Validation Methods Based on Federated Learning.

[20] Yiting Hong. Differentially Private High-Dimensional Business Data Publishing and Analysis Algorithm. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 28-35.

[21] Xu, D. (2026). Analysis of the impact of video infrastructure optimization on large-scale content quality improvement.

[22] Chen M. Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing[J]. 2025.

[23] Pan, H. (2025, March). Research on Efficient Computing Model of Hartree Fock and Density Functional Theory Based on GPU Acceleration. In Doctoral Symposium on Computational Intelligence (pp. 485-496). Singapore: Springer Nature Singapore.

[24] Wu, W. (2025, June). Construction and optimization of intelligent gateway software management platform based on jenkins cluster management under cloud edge integration architecture in industrial internet of things. In International Conference on 6G Communications Networking and Signal Processing (pp. 633-645). Singapore: Springer Nature Singapore.

[25] Wu Y. Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization[J]. 2025.

[26] Wu Y. Optimization of Generative AI Intelligent Interaction System Based on Adversarial Attack Defense and Content Controllable Generation[J]. 2025.

[27] Sun J. Quantile Regression Study on the Impact of Investor Sentiment on Financial Credit from the Perspective of Behavioral Finance[J]. 2025.

[28] Wang Y. Application of Data Completion and Full Lifecycle Cost Optimization Integrating Artificial Intelligence in Supply Chain[J]. 2025.

[29] Yu, X. (2026). Strategy Models and Practical Research of Growth Marketing under the Background of Digital Transformation.

[30] Hou, Y. (2026). Research on BIOS and BMC Compatibility Optimization Methods for Cross-Generation Servers in Production Environments.