Wealth Management: Clients and their Satisfaction

Evaluation Point for Banks and Wealth Management Arms; Does WIPRO offer some food for thought?

Ever wondered how people involved in wealth management are evaluated. It should be a no brainer: a wealth manager should be evaluated based on the basis of money made by clients through his investment. If not entirely, but a larger chunk of weightage must be given to this parameter.

Unfortunately, that ‘s not the way it is, definitely not in India. Investment Advisors / Wealth Managers / Relationship Manager, whatever you call them, are evaluated on the basis of the money they earn for the bank and not for the client.  Having been in the industry for just a little under 11 years, not very high on experience some may say, the way business models are run for these firms surely needs to be looked into.  The latest public outcry, which resulted in “Occupy Wall Street” kind of hysteria all over the world, was driven against this very notion of banking system. But it is the HNIs / retail investors that I am more concerned about.

For the wealth management industry to survive and thrive in India, it must change its business model and be more client-centric than revenue centric. Today, all institutions are concerned about growing their profitability through their client base irrespective of how client’s investments have grown or how satisfied the client is with the advisory offered. May be industry should take a leaf out of Wipro’s latest change of benchmark for employee evaluation where bonuses and pay hikes are now getting linked to customer satisfaction levels and not only with revenues or profits earned.

It will be a game change in the world of investment advisory. The financial distribution model in India has seen a host of changes over the last few years, courtesy SEBI, not sure if all benefited the industry. The latest discussion paper brought out by SEBI is probably a step in the right direction, where in it is seeking to make a distinction between agents and advisors and accordingly change the revenue model. In the advisory model the advisor will make money only from the fee levied to clients and technically the client will pay only if he sees the performance to his satisfaction.  SEBI on its part would have brought out this discussion paper on seeing the way advisors were treating the investors.

I guess its time that financial intermediaries, especially the reputed names, start something akin to WIPRO. Once bonuses, pay hikes get linked to the satisfaction of the client there will be onus on the advisor, a) to deliver results, and b) to keep him updated on latest happening in financial markets, which will help him in meeting the first (a) objective.  It will also ensure that we have quality people in the industry, and not someone who has just graduated from an A, B or C category MBA institute, who himself has never made any investment in the market, start advising HNIs with lacs/crores invested in financial instrument.

It’s a long way to go, but in the long run it will help everyone: clients, who can think of achieving their financial goals with probably less stress, keeping the market movement aside, and the advisory firm which will have a predictable, sustainable business model and of course a strong client base.

Finance Train Premium
Accelerate your finance career with cutting-edge data skills.
Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.
I WANT TO JOIN
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Saylient AI Logo

Accelerate your finance career with cutting-edge data skills.

Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.