Time-series pattern mining in SWIFT logs using AI-driven sequential embeddings built in python
Sappa, Ankita
Sappa, Ankita
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2026
Type
Book chapter
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Keywords
Anomaly detection in financial transactions; Sequential embeddings; SWIFT log analysis; Time-series pattern mining
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Citation
Sappa, Ankita. Time-Series Pattern Mining in SWIFT Logs Using AI-Driven Sequential Embeddings Built in Python. In M., R., Subrahmanyam, S., Subramanian, R.R., & Karthikeyan, J. (Eds.). (2026). Adaptive Technologies for Sustainable Growth (1st ed.). CRC Press. https://doi.org/10.1201/9781003739937
Abstract
Analyzing financial communication streams like SWIFT logs can provide insight into potential anomalies, compliance issues, or operational inefficiencies. Traditional time-series analysis techniques often overlook the complex structures and latent semantics within interdependent message flows. Here, we describe our work on designing a Python architecture for mining temporal patterns in SWIFT logs employing transformer-based sequential embeddings. Our method divides the message flows into time-windowed sequences, transforms the raw SWIFT fields into contextual embeddings, then applies supervised anomaly detection and unsupervised clustering to high-risk behavioural motif extraction. We tested our model on a dataset containing 3.1 million real-life SWIFT messages over an 18-month period. The hybrid transformer model proposed in this work surpasses the traditional LSTM autoencoders and baseline token classifiers, achieving a 94% F1 score in anomaly classification. The embedding clusters and temporal heatmaps visualizations show the model’s known compliance flags alongside previously hidden irregular patterns. Moreover, the system achieves real-time performance constraints, processing message batches with very low latency while adapting to changes in the streaming data. These results are promising for leveraging powerful sequence models in discovering intricate patterns in financial transaction logs and indicate potential advancements in AI-driven predictive compliance and risk analytics within SWIFT institutional frameworks. © 2026 selection and editorial matter, Dr. Raja M., Dr. Satya Subrahmanyam, Dr. R. Raja Subramanian and Dr. J. Karthikeyan; individual chapters, the contributors.
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Publisher
CRC Press
Journal
Adaptive Technologies for Sustainable Growth