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.
Essentially, there are four steps to overcoming the challenge:
The alchemy is meeting these ostensibly incongruous demands through one dataset.