Near Real-Time Anomaly Detection in Customer Transactions Using Lambda Architecture
Author(s): Ravi Kiran Alluri
Publication #: 2508005
Date of Publication: 04.12.2019
Country: United States
Pages: 1-9
Published In: Volume 5 Issue 6 December-2019
DOI: https://doi.org/10.5281/zenodo.16883295
Abstract
Detecting anomalies in customer transactions has become a cornerstone of financial security and fraud prevention, especially with the rise in digital payments and real-time financial services. Traditional anomaly detection systems often fail to capture subtle or evolving fraudulent behavior due to their batch-oriented nature and delayed data analysis. This paper proposes a scalable and robust approach to near real-time anomaly detection using the Lambda Architecture. This paradigm blends batch processing, real-time streaming, and serving layers to achieve high-throughput and low-latency analytics. The Lambda Architecture enables continuous ingestion and analysis of transactional data by integrating distributed stream-processing systems such as Apache Storm and batch frameworks like Hadoop with a unified data model.
The proposed system utilizes a combination of statistical profiling and machine learning-based models to identify transaction anomalies as they occur. Historical transaction data is used in the batch layer to train and periodically update models, while the speed layer processes incoming data streams for immediate anomaly detection. The serving layer unifies outputs from both layers to ensure consistency and accuracy. Feature engineering strategies include temporal aggregation, transaction frequency, merchant category codes, and location-based deviations.
Keywords: Near Real-Time Anomaly Detection; Lambda Architecture; Streaming Analytics; Fraud Detection; Customer Transactions; Apache Storm; Batch Processing; Financial Security; Machine Learning; Distributed Systems.
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