A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems

TL;DR

This paper presents a feature engineering method that uses processing times between message exchanges to detect failures in distributed instant payment systems, improving early detection, localization, and interpretability.

Contribution

It introduces a novel feature engineering approach based on message processing times for anomaly detection in payment systems, bridging technical and business visibility gaps.

Findings

Effective detection of diverse anomalies in TIPS system

Reduces investigation time for payment failures

Provides interpretable insights into system issues

Abstract

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.