A more capable firm

This is perhaps a case of managing success. Having more clients as well as more investments can be like trying to be in two places at once. More people are needed!

Wealth managers of all types face a productivity challenge. Costs are a “step” function where payroll costs are linear as business grows. Yet competitiveness requires shifting people from back office to client facing.

Digitisation is key. We are working on a project to integrate machine reading and machine learning with our database to automate and accelerate the investment due diligence process.

Balancing costs versus capabilities

A wealth manager that decides to increase its fund coverage from 20 to 40 funds may need to double the size of their investment team, based on the required tasks and time commitments.

One rule of thumb is one analyst can cover 20 managers – if that’s all they do. We estimate it takes 206 days/year for an analyst to cover 20 alternative funds.

20 managers

Adding 20 more funds takes 288 days/year (chart below).

40 managers

Problem: Each analyst has just 235 workdays per year available. With other duties this may only be 160 days. So proper coverage requires a dedicated team of 2 or 3 analysts for 40 managers. Costs can rise as fast, or faster, than coverage.

Step function

Expanding alternatives due diligence is essential to remain competitive. This means either hiring new analysts or finding a way to do more with an existing team.

[Our assumptions: 1. Person days/year for 5 key research tasks per fund: On-going due diligence 4 days; travel 8; new manager search 20; and initial due diligence 5. Portfolio management is 4 days/year/client. We assume 20 clients and 40 funds.]

A technology solution

A data-centric due diligence process offers the opportunity to improve speed and scope: 65% less time and 3x coverage.

Machine reading and machine learning offer the opportunity to facilitate data acquisition, interpretation and sharing throughout the value chain. Current marketplace offerings are often in a “silo”, requiring manual data “sorting and scrubbing.”

Technology solution2

Our project is called Takaya.io. We believe collaboration and interoperability is key to unlocking productivity gains. When our research and development is complete, we will ideally offer common “data pooling” that easily adapts to other programmes via APIs and other direct links.

We aim to enable our clients to help themselves as much as they wish, to retain total control over their investment decisions, and for us to support them with bespoke consulting whenever we are needed. This is our vision for collaboration.

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