With the continued acceleration of asset management’s digital transformation, many managers have begun fully automating the routine, resource-intensive operational burden of reconciliation. However, some managers have opted to settle for disparate and semi-automated reconciliation systems, which can increase operational risks and oftentimes opportunity loss due to the unclear picture it presents on resident cash. Read More
In this environment of information overload and increasing operational complexity, understanding the gaps between a client’s internal financial system data workflow and the counterparty or administrator’s external data is crucial for ensuring the integrity of positions held. With both artificial intelligence and machine learning-enabled capabilities, a position reconciliation solution can be used to automatically match all equivalent positions and ensure break management on a daily basis, ultimately helping funds maintain efficiency, accuracy and flexibility.
For any hedge fund or alternative asset manager, the day starts with reconciling positions, cash balances and transactions amongst internal portfolio accounting systems and external counterparties, including prime brokers, custodians and fund administrators. At its core, this daily task is operationally complex, making the use of manual Excel-based processes inefficient, error-prone and a drain on overall productivity. Due to this complexity, along with the fact that managers have to routinely deal with high transaction volumes and esoteric instruments, many are now seeking solutions that systematically analyze the root cause of repetitive exceptions, automatically escalate aging exceptions, establish multi-level approval workflows and ensure a robust audit trail of all actions.
With that being said, establishing a simple and unified reconciliation output across funds and brokers is emerging as a critical requirement for asset managers, and it can be done by implementing a solution to perform the below set of core processes and steps: