Operational risk and its mitigation is a pertinent topic right now. The new regulatory environment is forcing asset managers to adhere to strict operating and reporting standards; investors are demanding tight operational controls to protect their assets; managers are seeking to minimise middle and back office costs whilst delivering against these growing demands. Superimpose a growing instrument complexity, activity spread across multiple service providers and increased trading frequency and it soon becomes apparent that a new approach is required to manage operational risk and keep costs low.
Given the fragmentation of the trading process both in terms of service providers and systems and often weak information exchange between investment manager, counterparty, prime broker and administrator, one of the primary tools in the armoury of the operations function is inter-system reconciliation. Simply, is the data our traders see in the order management system actually what is held by us at the bank? Do the reports we send to our clients tie back to the equivalent sent by the administrator; will they question us as to why they are different?
Whilst failings in some areas are embarrassing, in other areas it can be responsible for a loss of hard earned P&L, an irreversibly damaged reputation or even for attracting the attention of the regulatory authorities. Timely reconciliation between all systems involved in the process and effective management of the differences is vital. Systems to match data, identify breaks and support remedial activity have been available for many years and are a base requirement for all firms involved in the trading process. However, in our view, what is available falls far short of the goal of full automation, fails to deliver the risk information needed to properly manage the process, and neglects the opportunity presented by the system’s privileged position within the operational infrastructure.
This paper reviews developments in reconciliation beyond matching and workflow and references the work of Watson Wheatley in delivering value-added products to this critical area.
A manual or semi-manual reconciliation cannot hope to deliver the required operational control and mitigation of risk required. Users will be pre-occupied with the process and will be unlikely to spot the risks hidden within the data no matter evaluate them and communicate the impact to the stakeholders.
Automating a reconciliation is no easy task. Obtaining a standardised set of reconciliation data from all systems just doesn’t happen. Data is provided in files of differing format and content at different times and to different standards. Normalising this data into a structure that allows reconciliation is a non-trivial task; there are firms dedicated to providing this service at a price so it can’t be easy. Data needs to be moved between remote servers securely and reliably. The files may then need to be decrypted or unzipped before they can be read. More difficult is to parse the now readable files, normalise, aggregate, enrich and write to a database structure, all in a highly transparent way so it forms part of the all essential audit trail.
Once the data has reached this point the automation becomes easier. Check for data integrity and reject it or repair it if it fails. Ensure all the data for a given reconciliation is present. Run the relevant match rules to isolate breaks. Annotate breaks with reasons and actions and present to the users; all within the scope of most reconciliation tools.
The process of automatically handling exceptions and correcting source systems is more difficult. The workflow tool must be taught to route breaks by type or severity. Some breaks may be a natural consequence of timing differences between systems and can be “parked” until the expected matching transaction arrives. Some can be “passed through” to the source system to correct a mis-booked trade or missing cash flow. Others need to be reviewed by users authorised to make a decision. Automating these elements of the process places key demands on the reconciliation tool. Not all need to be automated, but if most of the tasks can be assigned to the computer, the process will be more reliable and the user is able to focus on remedial action.
Many securities reconciliation systems do not have a concept of risk measurement. Without this, it is difficult or impossible to determine priorities and the users may allocate the bulk of their time to the wrong events whilst the key ones are ignored. Since the reconciliation system is there to mitigate risk then the absence of an ability to measure this at the transaction level is a serious omission.
This topic is too involved to deal with fully here, but we believe this places a requirement on the reconciliation system to understand the principles of portfolio accounting, the relationships between trades and positions and cash balances and cash flows, and certainly an ability to understand book cost, market value and P&L. Without this the impact of a trade price difference cannot be assessed; the absolute difference in price provides no information about its materiality and the system may either match something that is important or fail to match the trivial. A cash injection arriving at the custodian and not posted to the portfolio accounting and order management system may not represent a material break on day one but a week later it could damage the reputation of the manager and certainly represent a missed trading opportunity.
Empowered with portfolio accounting logic the reconciliation system can understand the integrity of the data it receives, control the process of matching within an accounting or trial balance basis, define matching tolerances in terms of materiality and allow the system and users to priorities breaks.
It is this functionality that is defining a new breed of reconciliation tools capable of providing highly automated operational risk metrics to the business allowing minimal resources to focus on what matters.
The primary focus of a reconciliation system is the day to day management and mitigation of operational risk. Consider the value of retaining this information over a period of time and having access to tools to summarise, slice and dice the data. Given the system’s central position in the trading process patterns will emerge that are entirely due to the strengths and weaknesses of the process itself. A longitudinal analysis can provide a primary input to improving the process, and of course, to determining the effectiveness of any treatments applied. The data can be partitioned by source system, user, or service provider to deliver quantitative performance measures; particularly useful when reviewing service quality of your suppliers.
Such information can be used to improve the effectiveness of the reconciliation process itself. New match or assignment rules can be added to isolate commonly occurring conditions thereby reducing the amount of manual intervention and increasing the overall level of automation.
What has been described is a very different set of requirements for the reconciliation system if it is to provide an intelligent tool for effectively and efficiently mitigating risk in securities trading operations. This is functionality that cannot be tacked on to back of a portfolio accounting system, and certainly should not be attempted in Excel. The availability of this new style of reconciliation properly deployed is able to provide a much more complete solution to the problem, providing valuable information to the operations department about real risks, and providing a much higher level of automation to the business.
By Duncan Wheatley, Managing Director, Watson Wheatley