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Why a robo advisor can be a good assistant to a human advisor

In this article, I expound my opinion that a single robo advisor exclusively built for a single advisor or trained with a single advisor (in the case of machine learning) can assist that advisor given enough time, input and practice.

A few weeks back, SEBI RIA Swapnil Kende wrote an eloquent piece on Why good quality financial advice does not scale. In this article, I  argue that a company of one financial advisor can free up its time with the help of robo advisory. Whether they scale their business (relatively) with such time or use it for self-development is a personal choice.

I want to refer to SEBI RIAs Swapnil Kendhe and Avinash Luthria as companies of one. After one of my favourite books: Company of One: Why Staying Small is the Next Big Thing for Business. That is, there is just one person in their company or team – themselves, and they personally handle all aspects of the financial plan. A company of one’s key attribute is that they do not measure success in terms of growth and prefer leisure and self-improvement over business growth.

In the present context, it also means that these RIAs have positioned themselves to solve complex problems such as this – Financial Planning Case Study: A complex asset-allocation decision – and to educate clients to become DIY investors.

I want to clarify that I am completely aligned with Swapnil’s views in the above article. If I were an advisor, I would adopt the exact approach (with as much human-validated automation as possible). So there is no disagreement between Swapnil/Avinash, and me. I also believe that good financial advice does not scale. Just that I think the limits vary from method to method.

Assume for the sake of argument that I am an advisor with experience in preparing financial plans for, say, 100-plus clients. Assume that I have recorded all my financial plans and interactions with my clients and with all my prospective clients (some who have rejected me and some who I have rejected).

I employ a skilled analyst to analyse all this data and make inferences. After enough time, she is ready with her results. She lists the qualities of individuals who would make good clients for working with me. Or those who would make bad clients and can be rejected.

She then bins the advice based on age bands, goal tenure, risk profiles etc. She then makes multiple copies of the freefincal robo advisory tool and creates templates for each bin. Say one for a 30Y old, one for a 50Y old etc.

The next time a prospect emails me, she uses the checklist to determine if the prospect can be onboarded as a client or politely rejected. Naturally, this will be verified and validated by me.

If the person can be taken on as a client, she determines which bin is most suitable and draws up a financial plan from the corresponding copy of the robo advisory tool. Again this is verified and modified as necessary by me.

With multiple iterations, this process will begin to run smoothly. She is not creating a financial plan by herself. She is creating a plan based on recognising my patterns.

All professionals can be reduced to a set of patterns if we observe them for long enough. I believe there is nothing wrong with using them for financial advice to free up the professional’s time.

Please note: In this model, The advisor verifies all aspects of the plan and adds necessary detail before it is sent to the client. The human touch is not reduced or diluted in any manner—only the advisor interacts with the client.

This is why I believe that bespoke robo advisory can be an effective assistant to that advisor. In time, the quality of the robo advisor should improve to a point where human intervention is minimal or even unnecessary for standard client profiles. However, human validation is never abandoned.

The above is a crude depiction of robo advice based on recognising patterns of one particular advisor. No matter how much an advisor claims he is capable of offering unique advice to clients as per their needs, we can always boil it down to patterns because the training and specific likes, dislikes and preferences,  can be identified if there is enough data (this is the big problem, not the robo advice itself).

While the above talks about a human (either the advisor or a delegate) spotting the patterns, it can be more efficiently done with machine learning. Today several organisations use AI-powered chatbots (e.g. Indigo Airlines). I would wager that a machine learning code can spot patterns in the advisor-client interactions at a fraction of the computational power of ChatGPT.

If I were a financial advisor, I would automate as many tasks as possible to focus on improving client interactions. After enough interactions, this will free up enough time without compromising the quality of the financial plan.  This time can be used for leisure, self-improvement or scaling up the business. Naturally, this will also have limits that someone can optionally test.  If I disagree with Swapnil, it is this: Wanting to scale is not bad. How we do it is another matter.

There is yet another aspect to the whole issue. What about employing young planners and training them in plan creation with guidelines? The chief planner would still validate all plans and interact with the client. Is there anything wrong with this?

Of course not! I would argue that professionals must nurture youngsters. Doctors do it. Lawyers do it. Academics do it. CA’s do it. So why not financial planners? Not every planner can strike out on their own from day one. So one can even argue that senior financial planners must mentor the younglings.

Will the quality of financial planning suffer if done this way? It could suffer if the chief advisor delegates too much. It need not suffer if there is enough verification and the client only interacts with the chief advisor. Of course, this would mean there is a limit to the number of clients serviced. So again, there are limits to scalability but with a bit more room.

Because of their training, human assistants will likely use their initiative while creating the plans. This is essential for their growth. Perhaps AI-powered models may do this as well!

At some point in future, can a Robo-advisor trained with a specific advisor replace that advisor? I always believed it could. Having spent enough time with ChatGPT, I am now positive. It is up to us how we leverage this tech and benefit from it (while initially suffering through it).

We live in exciting times! If we could afford a small machine learning code, we would make it read through all the 2000+ articles on freefincal and write variants (on request, which we would validate at a fraction of the time spent writing this article) and focus on more fulfilling creative pursuits. Long term readers would attest there are too many boring patterns in the articles we create here! I believe they would be better articles than I hope to write.

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About The Author

Pattabiraman editor freefincalDr M. Pattabiraman(PhD) is the founder, managing editor and primary author of freefincal. He is an associate professor at the Indian Institute of Technology, Madras. He has over nine years of experience publishing news analysis, research and financial product development. Connect with him via Twitter or Linkedin, or YouTube. Pattabiraman has co-authored three print books: (1) You can be rich too with goal-based investing (CNBC TV18) for DIY investors. (2) Gamechanger for young earners. (3) Chinchu Gets a Superpower! for kids. He has also written seven other free e-books on various money management topics. He is a patron and co-founder of “Fee-only India,” an organisation promoting unbiased, commission-free investment advice.

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Most investor problems can be traced to a lack of informed decision-making. We have all made bad decisions and money mistakes when we started earning and spent years undoing these mistakes. Why should our children go through the same pain? What is this book about? As parents, what would it be if we had to groom one ability in our children that is key not only to money management and investing but to any aspect of life? My answer: Sound Decision Making. So in this book, we meet Chinchu, who is about to turn 10. What he wants for his birthday and how his parents plan for it and teach him several key ideas of decision making and money management is the narrative. What readers say!

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