SmartStream Technologies won the best reconciliation management provider category at this year’s Waters Rankings, following up its multiple successes in recent years in the Buy-Side Technology and Sell-Side Technology Awards. Victor Anderson speaks to SmartStream’s Robin Hasson about TLM Reconciliations Premium’s perennial appeal to both sides of the industry, what the firm’s clients are looking for in a reconciliations platform right now, and its plans to incorporate AI and machine learning technology into its flagship offering.
WatersTechnology: SmartStream won the best reconciliation management provider category at this year’s Waters Rankings. To what do you attribute TLM Reconciliations Premium’s enduring success across both the buy side and the sell side in recent years?
Robin Hasson, product manager, TLM Reconciliations, SmartStream Technologies: I’ve been at the company for a number of years and have seen the evolution of the software from previous versions through to Premium. Although we started with domain reconciliations for cash and securities, we quickly identified that having a platform capable of reconciling any type of information is really what the market needed. So we established a standardized reconciliations process across cash, positions, futures, static, etc., coupled with best-practice domain features and exception management. Being able to easily customize and refine workflows and tailor the user experience are the other areas our clients like—for example, using the integrated workflow to solve edge-case but important issues.
Aside from the functions and features, as a team we challenge ourselves to keep the technology as current as possible. We invest significantly in our research and development to enable us to re-architect and innovate our engines and services, and this is most evident with TLM View, our user interface.
WatersTechnology: What are SmartStream’s clients most struggling with right now with respect to their reconciliations processes, and how is SmartStream addressing those challenges?
Hasson: People are looking for topical things like artificial intelligence (AI) and machine learning, but they’re also focusing on cost reduction right now. Banks are always looking to reduce their footprint in terms of cost, but there seems to be a particular drive to embrace an evergreen approach now—they’re looking to redesign their back-office environment as a whole and reduce their costs for the foreseeable future. That includes things like the type of database platforms they use, whether they are hosting applications in a public or private cloud, whether they can use a service for parts of their operations or not, and also reducing the number of individual components that were a given in years gone by. These long-established components, such as operating systems and databases, are being compared with newer options to establish a technical platform for the coming years. It is part of what people refer to as “digital transformation.”
The other area of high interest is the move to standardize all reconciliations by replacing all spreadsheets and manual tasks with a single tool. This is not new, but the drive to enable the business themselves to manage this process without the need for IT support is.
WatersTechnology: I hear that SmartStream is getting ready to unveil a new product at Sibos this year in London that it believes will be a game-changer in the reconciliations space and which will feature extensive AI functionality. Can you give WatersTechnology’s readers a flavor of what it might entail?
Hasson: I can’t talk specifically about the new product right now, but we have been working on AI and machine learning in the last few years to help build and onboard reconciliations quicker. Clients want to be able to get a reconciliation from the receipt of data through to testing it within minutes. Through the TLM SmartRecs product that we launched last year, we can already do that without AI. We’re now layering AI on top of that, which helps analyze and map the data into the system and identify matches automatically.
We’re looking at AI across the board for machine learning, which we’ll be launching in the coming months. Machine learning will be used to improve many areas, including automating manual matches, improving exception categorization and allocation, and system monitoring.
Full end-to-end AI matching will not pass scrutiny with auditors in certain markets without some level of assurance that it has been matched correctly and accurately. So what we’re doing in Premium is incorporating AI to automatically find those matches as part of the standard model when you want it. Importantly, we then allow rules-based tests to assert the quality of those groups to show auditors that they meet the necessary criteria. This hybrid use of AI with rules is an example of the targeted, high-value innovation we are working into the product.