What are the most important considerations when using AI for robo-advisory services in FinTech?

When using AI for financial advice, following the rules is a big deal. Imagine each place having its own set of rules for financial services. Robo-advisors, like the one I worked with, have to play by these rules. Some places might want them to share how they make decisions and what they charge. Others might have financial requirements. In my experience, it's like making sure you follow the local traffic laws – each place has its own, and you need to know and obey them. Robo-advisors also need to make sure their AI is fair, clear, and keeps client info safe. It's about being transparent, accountable, and respecting privacy.

I have spoken with several consumers about automated or personalized finance and they get excited about the possibility that decision making would be done by the AI. (Portfolio allocation, sock picks, timing, etc.) Having been in the securities and commodities industry, this might create quite a lot of compliance and regulatory issues. I believe AI provides a tremendous opportunity for investors and advisors for success, but the investment decisions have to be made, ultimately, by the client and not the "robo-advisor".

When I used AI for financial advice, having good data was key. Think of data as the fuel for AI—robust, reliable, and diverse. In my experience, it's like cooking; sometimes, the ingredients are missing or a bit old. Robo-advisors need solid processes to ensure the data is clean and fits what their clients need. It's about keeping things simple, accurate, and personalized.

The populations largely and disproportionately affected by financial literacy tend to also not trust financial institutions, financial technology, as well as marketing motives. I think building technologies represented by a mirror-like figure is the best way to build genuine and organic commitments. Then from there, the balance of automation and human intervention can coexist harmoniously under the themes of trust and security. Additionally, not hiding UX opt-out features regarding marketing and info sharing.

Scalability is essentially the factor that makes or breaks the success of a robot-advisor or for that matter, any other AI application in Fintech. However, the architecture to be used, the algorithms being used and also the volume and type of data that is being sourced will be determined by the strategy that is being utilised as well as the targeted customers. For e.g. the Data and architecture strategy for an advisor targeted towards algorithmic trading for global securities will be very different from a passive investment management advisor. Also, one of the key decisions would be to identify a cloud strategy because given the extent of data to be analysed, a single cloud or multi-cloud strategy would need to be used.

Thank you Danish for giving perfect example and explanation 👏. I agree with you that scalability is important so choosing right infrastructure stack is crucial from the start and it makes total sense that what type of data you want to process through that system.

Consider creating clear definitions for when a real person should take over. This might be due to compliance issues or just making sure that complicated questions are answered probably. This will give a chance for next level advisory and customer contact.