Transforming Payment and Treasury Reconciliation Through Process Mining and Exception Playbooks

Abstract

Enterprise payment and treasury reconciliation are weakened by siloed systems, identifier misalignment, and time lags between payment gateways, internal and bank software, and accounting, and even inefficiencies in spreadsheet-based reconciliation processes with large exception backlogs needing manual processing. Process mining technology extracts event logs from transactional systems, reconstructs the actual reconciliation process, and diagnoses bottlenecks and exception patterns. The presence of domain knowledge improves the quality of anomaly detection through process mining compared to generic algorithmic methods. Exception playbooks codify how resolution is performed, using the process mining analytics captured by the vendor-neutral process, and are executed in Java enterprise architectures. Microservices implementations using event streaming systems enable high-throughput reconciliation. Distributed tracing and immutable audit logs help support regulatory compliance. Variability modeling techniques, on the other hand, enable the building of exception handling logic based on classifications. By linking process mining diagnostics to systematic exception management systems, automated reconciliation replaces reactive investigatory methods, thereby improving the control environment, increasing the speed of cycle-time, and optimizing the working capital requirement.