For asset managers the need to keep up with overnight transaction processing and complex data volumes is ever present.
This can no longer remain as a simple batch process as the operational risks are too high. How can improved workflow, exceptions based data management and faster processing help middle offices step away from process management and move towards value-added analysis?
Workload automation is no longer restricted to its simplest form of processing batches of information overnight. Today, it is about event-driven, fully automated business processes operating as close to real time as possible. This in turn drives greater efficiency downstream through the whole trade lifecycle, reducing errors, manual interventions, reconciliations, breaks and fixes.
“Clearly, an integral part of such compliance will be the ability of the firm’s investment management system – including the front, middle and back offices – to formulate an accurate picture of assets and liabilities in a timely fashion. This implies a greater degree of sophistication than can be achieved with end-of-day batch processing, let alone Excel spreadsheets.”
The asset management industry is driven by the demands of regulators across the globe through Dodd-Frank, AIFMD and MiFID. At the same time asset managers are looking to invoke a step-change in the quality of financial (profit and loss, funding and liquidity) and risk management capabilities. Being able to perform reconciliation of all the previous day’s transactions overnight has become both an operational and regulatory requirement as the markets move to ever shorter trade settlement cycles and the regulator demands more transparency.
In practical terms this means that if 500,000 transactions occurred during the trading day then some time after close of markets a computer process might start booking all those transactions. There are also some tasks in the front office trading system that cannot be performed on a real-time basis, because the activities must either be scheduled to complete between trading days, or were entered on previous days for later execution.
If this goes ahead without error then all is well. But key to being able to do this overnight processing effectively is being able to identify when and how things have gone wrong with an exceptions-based workflow. The right system that provides timely error detection and can automate error correction can help drive efficiencies, reduce cost, and help better organizations understand their data.
And in the asset management industry where trading needs to start again the moment the markets open, the implications of not being fully up to date are huge.
Getting it right
The importance of getting this right is amply demonstrated by RBS’s batch processing failure in 2012. In the RBS case the detective work needed to find the initial problem was nearly impossible and made worse as the record of transactions up to the point of failure was inaccessible. By the time the problem was isolated, the backlog was enormous, which created the long delay in getting operations back to normal. While this failure mainly affected the retail current account operations, it is a reminder to the industry that legacy systems pose an operational risk when it comes to long batch calculation processes.
An overnight processing capability that is both effective and accurate can go a long way.
It can solve the primary challenge of ensuring the efficient validation of trades, the integrity of trade booking and transaction data in a controlled and compliant operation. It can also mitigate systemic risk by reducing exposure between the parties to a trade, between the counterparties to the clearinghouse, and for the clearinghouse itself.
Accuracy and speed have always been watchwords in the asset management industry but are becoming increasingly more so in an efficient middle office. The overnight processing window has much to do in a small space of time. Getting this right is important, being able to see where things have gone wrong; even more so.
Need for Speed in the Asset Management Industry – Takeaways:
- Simple ‘black box’ batch processing creates operational risk.
- Current systems can’t cope with increasing data complexity and shorter calculation windows.
- Data management challenges need intelligent software to help remove manual processes.
- Automation, visualization, exceptions-based reporting and faster processing can help free time for more strategic analysis.