Daily and intraday reconciliation has become a key measure for improving asset managers’ internal controls. However, due to the rise in volume of reconciliations, teams are only given a small window of opportunity to identify and resolve breaks – igniting a process known as exception management that involves exception prioritization, escalation and documentation.
Both inherently time-consuming and complex, operations teams must manually assign breaks based on their priority or function to different users once labeled through a matching rule. From there, teams must go through a number of files and systems in order to identify the root cause of the breaks.
Fortunately, reconciliation systems have evolved over the years to remove this need for human intervention and help facilitate the immediate resolution of breaks. This type of system should not only support the process-based workflow management of breaks assignment and escalation, but it should also be sophisticated enough to identify exceptions’ root cause, aggregate similar exceptions and be intelligent enough to understand and mimic user actions over a period of time. Among these key requirements are several others to help simplify exception management within the reconciliation process, including:
- Automated investigation of cases for root cause identification
- Use of artificial intelligence and machine learning for break categorization and prioritization for complex data sets and vague identifiers
- Exception documentation
- Automated workflow for assigning breaks to designated users based on categorization and prioritization
- Exception aging for the escalation of breaks
The reconciliation process can only be efficient when there is a system in place to quickly identify and correct exceptions and improve match rates. Leveraging an integrated approach of technology and operations management, asset managers can use this system to gain better control, increase efficiency, mitigate risk and improve decision-making.