Financial services as an industry is a comprehensively regulated with a high reporting standard that must be met as part of the ordinary course of business. The larger the institution, the higher the level of scrutiny and the larger the compliance team required to manage the reporting workload.
For UK asset managers - whether that’s Investec, Charles River, Aviva, or Abrdn - one critical element of this regulatory requirement is the reporting of revenues for assessment. Revenues are typically made up of a blend of performance fees when the fund’s returns have exceeded a benchmark, and a management fee of up to 2%. They must be reported to and calculated by a 3rd party provider such as the Big 4 accountancy firms to avoid conflicts of interest.
Our client, an ESG-focussed asset manager with $50bn in assets under management (AUM), reported their fees to the 3rd party provider but owing to the complexity of how performance fees are calculated, still had to check the figures and reconcile them against their own records.
Fee reconciliation a legacy process that is both time consuming and fraught with regulatory risk: human error or miscalculation can have financial or legal consequences for the firm. QuantSpark was engaged to solve this problem: find an automated solution to an extremely manual process.
Whilst the first part of the engagement tackled that reconciliation automation, it also became clear there was an opportunity to expand the scope of the project. Fee forecasting, another historically manual process, was the firm’s measure of future financial performance whilst taking into account a wide range of variables from the sector-specific to market-wide shifts. The more accurate this forecast, the more informed the firm’s partners could be when it came to making key business decisions.
From this, QuantSpark expanded the automation work to create a simple, effective way of modelling revenues with minimal manual upkeep and enhanced logic.
Our team began with the files that came in from the 3rd party provider and conducted a simple version control exercise, merging the reports into a central database so they could work from a single point of truth rather than contend with multiple reports.
The client’s chief concern was to arrive at a single, correct figure for their fees that could be attributed to each of their limited investors. For our team this was achievable by writing a simple Python script that captured the manual logic needed to compare the external reports with our client’s internal figures, automatically identifying any differences between the two. Once this was set up, the output could be visualised in a straightforward dashboarding suite. Our client used their own proprietary analytics platform but it could just as easily be created within an off-the-shelf product such as Tableau or PowerBI.
For the second half of this project, our team were able to build on their existing work and reuse the same Python script, adapting it to this additional use case to save the client both time and money.
Our analysts began by working with the client to understand the wide range of variables that could impact the firm’s financial performance, from sector influences to market corrections. Then, by creating a dummy environment – replacing real data with test data – our team could create a number of scenarios to test the impact of those variables on the client’s forecast revenues and validate for accuracy before submitting to the client for assessment.
The scenario mapping was designed for flexibility: once uploaded to our client’s platform their business teams could choose from a selection of preset options, applying the different scenarios to their annual forecasts, or drill down into granular quarterly performance up to five years in advance, and assess the factors that might impact future revenues.
Financial services firms have long managed regulatory burdens and assessed their own performance through Excel. While Excel is commendable in many ways, for the modern money manager it can easily become an unwieldy and time-consuming way to understand today’s results and tomorrow’s forecast.
As a result, automation is increasingly becoming a necessity within financial services and beyond. Rather than spending analyst time checking reports line by line, QuantSpark’s solution clearly highlighted any delta between our client’s own reporting and the external provider’s. Beyond the immediate productivity gains that offered, automation can also guard against simple human error and the unfortunate consequences that can cause.
Equally when it comes to forecasting there are clear benefits to creating a modern, analytics-driven solution. An automated scenario tool gives business the ability to take a top-down snapshot approach to their future P&L assessment or to work bottom-up at a granular level, examining the performance of individual funds or business lines and the variables that might have an impact. By combining accuracy and reliability of information, analytics can create a solid foundation for any business decision making.