Artificial Intelligence, Machine Learning, and Data Engineering for Digital Repair Twins in Modern Payment Ecosystems

Abstract

Modern payment ecosystems have grown into sprawling, latency-sensitive networks of banks, card schemes, digital wallets, payment gateways, and regulatory intermediaries, all of which must remain continuously available while resisting fraud, downtime, and compliance failure. This paper introduces and formalizes the concept of the Digital Repair Twin (DRT), an operational extension of the classical digital twin that is purpose-built not merely to mirror a payment system’s state but to diagnose faults and autonomously propose or execute corrective actions. We examine how artificial intelligence (AI), machine learning (ML), and modern data engineering practices converge to make DRTs feasible at production scale. The paper presents a layered reference architecture spanning data ingestion, feature engineering, model serving, and closed-loop remediation, and it discusses supervised and unsupervised learning techniques for anomaly detection, root-cause analysis, and predictive maintenance of payment infrastructure. A simulated case study involving a card-not-present transaction pipeline illustrates how a DRT can detect a degrading authorization success rate, isolate the responsible microservice, and trigger a self-healing workflow within seconds.