Painful to produce and impossible to rely on? The challenge of calculating AUM & Flow

Whilst AUM & Flow data is undeniably critical for a firm both operationally and strategically, it’s fair to say that most asset managers still perform the calculations manually on spreadsheets. Why is it so difficult and is there a better way? 

Most asset managers calculate AUM & Flow as an operation in two broad areas of the business. The first location is in Distribution (or Sales & Marketing) and the second is in Finance – both for their own purposes. Immediately, you have the potential conflict of calculations being performed twice.

Furthermore, the assessment by the Finance team will be heavily product focused but will struggle to deliver the client perspective on the assets and flows, whereas Distribution will be seeking a client or investor view of the world. Once again, there is a conflict of interest through varying methodologies.

Operatives in both locations will be on Excel, using data files sent from multiple third-party outsourcing firms, such as transfer agents (TA) and fund accountants. A global firm which has grown by acquisition may have a number of these across its product range – it’s not uncommon for large asset managers to have perhaps three fund accountants and four transfer agents.

These data files will be received by email and contain thousands of rows of data. Typically, there will be a poorly maintained workbook that has evolved over many years that performs lookups, and lots of gaps in the figures because the data is difficult to knit together. The data will also be of varying quality and arrive at different times throughout the month. Some data will be received daily, some monthly, some five days in arrears, some a month in arrears. Somehow, all of this disparate information has to be pieced together with little or no consistency in the logic being applied.

Say your business has launched some new funds. You need the detail of these new funds and those share classes in your spreadsheet. The product team and the data team may have the information required, or it may be elsewhere. After the month end, new AUM & Flow figures will immediately be needed by various departments. Distribution and Finance will both run out of time to include the new funds in their figures. The numbers that are presented to the Board will be ‘best guess’ valuations.

Another example is in Distribution. A German retail sales team will be able to identify their clients that are visible on the TA register (since they will have a German address), but what about clients that invest through a platform? To include these, data needs to be analysed from the platforms to understand which of the underlying clients belong to the German sales team. Automating this look-through across many platforms is both manually intensive and time consuming.

It becomes clear that, working this way, it is impossible to form a global picture that is of any use to the Head of Distribution. Firms will often receive multiple files from the various transfer agencies and stitch it together in Excel to give a ‘global AUM & Flow report’. Painful to produce and impossible to rely on.

The whole process of generating AUM & Flow data involves inefficiency, duplication and operational risk. Compounding the problem, most asset management firms do not have sufficient resources devoted solely to this task as it falls between various roles and responsibilities.

For the CEO seeking to strategically decide whether one part of the business is performing well compared to another part (e.g. wholesale vs institutional), discrepancies in this AUM & Flow data is misleading at best and destructive at worst. Loss making products may escape detection and significant market opportunities be missed.

The relationship between AUM & Flow is therefore symbiotic. For example, if all the money flowing toward a business one month is into low fee products and all the money flowing away is out of high fee products, a firm could be in the position of being net positive in terms of AUM, but revenue could be negative.

Is there a better way to calculate AUM & Flow? How does the approach need to change?

Essentially, there are four steps to overcoming the challenge:

  • Step 1 – Dispense with the siloed approach of calculating AUM & Flow. The figures have to be produced in one location only.

  • Step 2 – A simple process must be established for data aggregation, data sourcing and data integration.

  • Step 3 – Normalising the data so that it can be joined together.

  • Step 4 – A well-documented methodology for turning the raw data into this AUM & Flow ‘golden copy’ dataset. This methodology needs to be very clear, unambiguous and produce results that work for the seemingly contradictory needs of Distribution and Finance. Those conflicting needs include speed of delivery, flexibility and perspective.

The alchemy is meeting these ostensibly incongruous demands through one dataset.