Spending, Saving and Investing, Automatically
Autonomous finance is a financial service that automatically conducts personal finance management based on algorithms, rather than manual human input. Essentially, a firm can manage their client’s spending, savings or investments automatically, without the firm or even the client manually submitting information. This service is typically available within two sub-sectors of banking: Retail Banking and Wealth Management.
On the retail banking side, a firm can set up a client's checking account to automatically pay certain bills at a certain time. Also, based on a client's budget or threshold, a firm can automatically transfer funds from a client’s checking account to her savings account on a regular basis.
On the wealth management side, autonomous finance is essentially robo-advisory, which is automatically allocating a client's funds to investment opportunities based on an algorithm instead of a traditional financial advisor. Robo-advisory is a bit dated with companies like Betterment and Wealthfront entering the industry in the early 2000s. However, it wasn’t until 2016 when firms like Goldman Sachs entered the space. As of 2020, Wealthfront expanded its offering beyond automatic investments to automatic savings in a product called Autopilot.
I anticipate that larger financial institutions will increasingly offer autonomous finance platforms to their customers, integrating retail banking and wealth management services. This trend is driven by the desire to enhance customer experience and foster loyalty.
According to a Salesforce survey of nearly 2,800 global financial services leaders, here are the top use cases for autonomous finance.
For retail-banking leaders:
The top priority is to automate account transfers. Customer behavior can determine the frequency and amounts of transfers depending on factors such as balances and goals.
Second on the priority list is offering personalized product recommendations. As AI technology is typically used for these autonomous finance applications, the algorithm can learn more about the customer as they continue to use their debit card, for example. Patterns and insights can develop from customer spending habits, which would enable a bank to recommend products accordingly.
The third priority here is automatic pre-qualification. As a firm continuously understands customers goals, income and spending habits they would have a better understanding of the customer and would be able to easily determine their eligibility for credit.
For wealth management leaders:
The top priority is financial advising. A robo-advisor would use a client's investment goals to help determine her asset allocation.
Portfolio optimization is the second priority. This can be done through automated savings or portfolio rebalancing based on risk tolerance, market conditions and other factors.
In 2017, the Royal Bank of Canada partnered with a company called Personetics to launch an autonomous finance offering. Two products came out of the partnership: NOMI Insights and NOMI Find & Save.
NOMI Insights provides personalized and timely insights to help clients manage their day-to-day finances on RBC’s mobile app instead of clients using spreadsheets. The Find & Save product helps simplify savings by using predictive technology to find amounts of money clients can spare, and automatically save it. As a result, engagement on RBC’s mobile app increased and clients’ savings doubled on average.
Although the value of autonomous finance is clear, there are a few concerns.
The upside of autonomous finance that is more certain is delivering enhanced customer experience. For customers, there is less manual effort, they save time and receive proactive customer support instead of reactive service. For firms, there is less human error, time savings and a reduction in labor costs.
However, the downside is the lack of customer trust. Autonomous finance relies heavily on personal information to make things work to the greatest extent. However, customers are not always willing to give up their private information. People are comfortable using an algorithm for driving directions but less so when it comes to finances as it is unchartered territory. The key for firms would be to prioritize transparency on how customer data would be collected and used.
Another upside is firms’ access to customer data. As mentioned earlier, this can lead to a greater level of personalization, which then can drive upsell and cross sell opportunities. The uncertainty is around how much of that data would be accessible and the data integrity on a granular level.
The downside which is less certain is the potential for changes in the regulatory environment. In the US, AI in general does not have a structured regulatory compliance framework, which can change, thus potentially altering the current applicability of the service.
While autonomous finance is still in its early stages, it represents a promising direction for the industry. As companies increasingly focus on customer-centric strategies to adapt to the digital age, I anticipate that U.S. banks will soon offer this service to both individual and small business clients.
-Kiswana