Data Management & Quality: The importance of a data management and quality layer is very clear – people make the right data-driven decisions if the data they are using is correct. A typical reconciliation system extracts data from different sources to validate and enrich the data. However, maintaining the data sanctity and quality while processing the data is becoming increasingly more challenging with potentially significant downstream consequences. If a single rule erroneously aggregates incoherent data or rejects the desired data, a ripple effect can cause thousands of records to be inaccurate.

Central to any data management system is its ETL process. Despite significant efforts and investments to improve the technology and quality of ETL systems, the current and most prevalent process is still dependent of herculean efforts of manual ad hoc research and diagnosis to identify and correct issues.

Matching: Two major functions of this layer are to match the correct data from the internal side with the external side based on a set of matching criteria and to label the non-matched data with enough information for the user to take next steps. For asset management, the reconciliation process must happen at the most granular level of data, spreading across different investment products and account structures. Therefore, the system must be capable of handling the complex structure of data and should facilitate matching criteria to match even the most transactional information while maintaining the required structural hierarchy.

At the same time, it’s not always possible to define matching criteria and to 100% match the data from two completely different sources. With this, individual breaks must be matched manually, which effectively defeats the purpose of an automated system. Hence, the system must not only provide the perfect matches by rules-based criteria, but it should also understand the underlying functional structure of the data being reconciled. This provides intelligent insights to reduce manual efforts as much as possible.

Exception Management: For most asset managers, intraday reconciliation is becoming a key measure for internal control, and, therefore, they get a very small window to find and resolve breaks. Once a break has been labeled through matching rules, the operations team’s next task is to assign the breaks based on their priority or function to different users. The users then go through a number of files and systems to identify the root cause of these breaks, which often involves intensive human intervention.

An efficient reconciliation system should not just support process-based workflow management of breaks assignment and escalation – it should also facilitate the quick resolution of breaks by providing powerful inter-system communication modules. The system should be sophisticated enough to identify the exception root cause and aggregate similar exceptions while being intelligent enough to understand and mimic user actions over time.

Reporting & Analytics: Reporting is not a key feature of a reconciliation tool, however; effective reporting can result in an untapped competitive advantage. In most hedge funds, once the operations team completes the reconciliation process, they collect data from different systems to compile various management and compliance reports. Producing efficient, easy to read and high-quality reports will provide full visibility into cash holdings, transactions and trial balances, resulting in effective decision-making across firm-wide functions.

An efficient reconciliation system can provide managers with a competitive edge,  however, many current, prevailing reconciliation systems remain highly dependent on manual intervention. Therefore, asset managers would be wise to seek and demand a high degree of automation in selecting a modern solution.

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