SmartStream
and identify where the most advantageous operational improvements can be made.
“At the core of the affirmation and settlement process is a highly performant and configurable matching engine,” says Shenai. “However, it is equally important that the matching system is supported by ‘business aware’ data validation and enrichment services so that match rates improve.” “Where there is missing reference data, such as securities or standard settlement instructions (SSIs), the system must offer effective exceptions management to work through the problem in the most efficient manner,” he adds.
Some research into post-trade processes suggests that many market participants are still conducting up to 20% of reconciliations offline using systems built in-house. In some parts of the market, that percentage could well be higher. Those manual processes greatly increase the risk of missed settlements and associated costs, so both buy-side and sell-side firms are coming to terms with the need to automate.
In doing so, banks may be able to look beyond the challenges that T+1 settlement brings and, perhaps, realise tangible opportunities to boost efficiency. The process of scrutinising technological infrastructure and working practices should, if done thoroughly, reveal precisely where inefficiencies reside, thus refining decision-making processes, and enabling banks to identify peers and service providers with quality data. Ultimately, banks should use this review to decide on a reliable, proven reconciliations system, which should be able to handle multiple asset classes, be volume- insensitive, and be capable of dealing with new and existing data formats. In an ideal world, it should allow the organisation to move away from the traditional end-of-day reconciliations processing and lead to the adoption to a real-time, intraday approach. That kind of efficiency delivers the competitive advantage that banks crave.
Product suites that can provide fully controlled reconciliations architecture are out there, as are solutions that add on comprehensive exceptions management and sophisticated reporting capabilities across all asset classes. Ultimately, they provide a holistic view of the data flowing in from the disparate sources that affect the T+1 settlement cycle. “Depending on the asset class or account set-up, the trade data from the front office may be received from a variety of internal systems and in many different formats,” adds Shenai. “Our reconciliations solutions, for example, offer unique, workflow-driven capabilities to not only normalise the data so that it can be matched, but also to enrich data by performing look-ups against your static and reference data so that there is never a mismatch due to nomenclature or missing data.”
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Putting the AI into reconciliations The product suite that Shenai describes is able to provide a comprehensive level of automation for data management and reconciliations because it relies heavily on the deployment of artificial intelligence (AI), as well as cloud technology.
Using a powerful AI engine, SmartStream is able to deliver immediate results from reconciliations process that once would have taken days or weeks. Securely hosted on the cloud, the solution is also able to learn and improve with every iteration as it incorporates observational learning functionality. It is designed to be faster, easy to use and increasingly intuitive. Banks will also need solutions that are asset-class agnostic and able to handle a wide range of reconciliation types. The T+1 environment will also require them to implement solid controls and comprehensive exceptions management capability. “We recognise that large volumes of data in a huge variety of non-standard formats and structures are still checked for accuracy and completeness using spreadsheets – or the tasks are not done at all,” notes Shenai. “The combination of our business expertise, gained over four decades with over 2,000 customers across the globe, and our dedicated Innovation Lab team – a collaboration of ultra-smart mathematicians and data scientists – enables us to deliver a tool that can match any data, for any reason, in an instant.” Regardless of the data format or how low its quality, AI is capable of reading, analysing, learning and identifying what needs to be compared. It can then present a list of unmatched records or disputes for investigation. Compared with burdensome tasks like Excel automation, transactional reconciliations, checking data between systems, managing exceptions and validating regulatory reports all become straightforward processes.
“Once you have put a system in place which automates, say, 99% of your trade settlements, that is still not sufficient, as that remaining 1% requiring human intervention may still expose you to significant risk,” Shenai remarks. “Where there is missing static or reference data, such as securities or SSIs, AI offers exceptions management capability to work through the problem in the most efficient manner. “An example of this is its ‘exception storming’
capability, which effectively avoids flooding the system with duplicate exceptions for the same problem, such as missing or invalid SSIs,” he adds. “Resolving the problem once – by, for example, adding the relevant SSI – can automatically progress all the stuck trades through the life cycle.”
Reconciliations need not be a stumbling block in the transition to T+1, and with AI used wisely, banks can, for once, get ahead of the curve, rather than running to catch up. ●
Future Banking /
www.nsbanking.com
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