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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.21710 |
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| _version_ | 1866909869528842240 |
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| author | Porcelli, Lorenzo |
| author_facet | Porcelli, Lorenzo |
| contents | Instant payment infrastructures have stringent performance requirements, processing millions of transactions daily with zero-downtime expectations. Traditional monitoring approaches fail to bridge the gap between technical infrastructure metrics and business process visibility. We introduce a novel feature engineering approach based on processing times computed between consecutive ISO 20022 message exchanges, creating a compact representation of system state. By applying anomaly detection to these features, we enable early failure detection and localization, allowing incident classification. Experimental evaluation on the TARGET Instant Payment Settlement (TIPS) system, using both real-world incidents and controlled simulations, demonstrates the approach's effectiveness in detecting diverse anomaly patterns and provides inherently interpretable explanations that enable operators to understand the business impact. By mapping features to distinct processing phases, the resulting framework differentiates between internal and external payment system issues, significantly reduces investigation time, and bridges observability gaps in distributed systems where transaction state is fragmented across multiple entities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems Porcelli, Lorenzo Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Instant payment infrastructures have stringent performance requirements, processing millions of transactions daily with zero-downtime expectations. Traditional monitoring approaches fail to bridge the gap between technical infrastructure metrics and business process visibility. We introduce a novel feature engineering approach based on processing times computed between consecutive ISO 20022 message exchanges, creating a compact representation of system state. By applying anomaly detection to these features, we enable early failure detection and localization, allowing incident classification. Experimental evaluation on the TARGET Instant Payment Settlement (TIPS) system, using both real-world incidents and controlled simulations, demonstrates the approach's effectiveness in detecting diverse anomaly patterns and provides inherently interpretable explanations that enable operators to understand the business impact. By mapping features to distinct processing phases, the resulting framework differentiates between internal and external payment system issues, significantly reduces investigation time, and bridges observability gaps in distributed systems where transaction state is fragmented across multiple entities. |
| title | A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.21710 |