The New Intelligence of the Financial Revolution
The Beginnings
In the 1980s, the floor of the New York stock exchange was a bustling cacophony of urgent yells, each attempting to overpower and out-pace the other in hopes of placing as many trades as possible before the closing bell.
The room was loud and chaotic, flooded with brokers, traders, and specialists, all staring intently at an electronic ticker that determined the fate of their client’s portfolio. Paper tickets, representing buy and sell orders, flew around the room in a blizzard, ending in a bank of paperwork on a clearing clerk’s desk at the end of the day.
Meanwhile, across town in a quiet two-bedroom, ranch-style home, a young couple sits at their dinner table. In front of them is a check register, a calculator, and piles of bank statements, as they attempt to balance, budget, and plan for their future.
In current times, this picture of the financial world is almost as unrecognizable as a hotel oil painting. Trades are placed silently and electronically, with a growing system of automation to assist. The anxious, caffeine-fueled brokers on the trading floor have been replaced by operating systems and algorithms. Our entire financial plan fits conveinently in the front pocket of our jeans.
However, we have yet to see the apex of the digital revolution. With the advent of large language models (LLM) and natural language processing (NLP) dominating how businesses innovate, we are already expperiencing the next phase of the process: the AI professional.
Automated Advising & Planning
Previously, the role of financial advisor required a significant amount of heavy-lifting and human interaction. They would spend countless hours meeting with clients face-to-face, recording their discussions, and curating a plan that was congruent with realistic financial goals.
This would require manually gathering market data, directly communicating with investment firms, and performing calculations to formulate strategies. They would need to keep a watchful eye over diversification, asset allocation, and rebalancing based on an ever-changing market.
Now, we have the robo-advisor, where advanced AI algorithms consume and evaluate massive amounts of data to create a customized investing portfolio. These bots can not only perform the manual calculations in a much more expeditious manner, they can also automatically diversify and reallocate based on the investor’s goals, preferences, and risk tolerance. Even environmental, social, and governance factors can and will be considered in the automation.
The continuous, 24-7 monitoring not only provides peace of mind that you will not lose your wealth while your advisor sleeps, it also serves to be a cost-saving measure. These savings have dramatically lowered the barrier to entry for investment management. While companies used to require high account minimums and changes expensive fees, the reduced need for labor has opened the financial services world to a wide variety of investors.
Balancing the Checkbook
For the vast majority of people, our financial goals and aspirations take a significant amount of planning. Buying a dream home, taking a dream vacation, and even experiencing that dream retirement life will simply remain a dream until a clear financial picture is formed.
Like the couple mentioned above, manually balancing their checkbook at the dinner table, this process used to involve piles of paperwork, manually created charts, and a hand-written reminder to stick on the refrigerator.
Luckily, that process can now be handled by the same tool we use to send text messages and share Instagram reels.
Budget and credit monitoring apps have been around for quite some time. However, the inclusion of AI has taken personal finance to the next level. We now have tools, such as Mint, that can automatically track and categorize transactions for multiple accounts, while also providing individualized advice for budgeting and saving based on preferences, history, and environmental factors.
Certain platforms will assist with staying on track by analyzing spending history and risk tolerance to automate allocating a percentage of one’s salary into a savings or investment account. We are even seeing a trend in the consideration of the emotional well-being of the individual, where patterns are identified and gentle reminders are given to keep going.
Imagine someone at your bank caring enough to call and remind you to keep consistent habits.
Fraud Detection
We live in a frightening world. One minute we are on track to get out of debt, pay for our child’s college, and purchase a new car; The next minute, after we swipe our debit card at a gas station or forget to change a password, hundreds or thousands of dollars go inexplicably missing.
For years, financial institutions have implemented some form of fraud detection. In the digital world, this often involved a rule-based system dependent on analyzing historical fraud patterns. While reasonably effective, they also required consistent monitoring and updating based on new patterns and were very slow to adapt to the ingenuity of the financial criminal.
AI-driven fraud detection is able to analyze extremely large transactional data sets, creating and refining patterns and reducing the instances of false positives. Much like other personal finance tools, they can and will consider the customer’s long-term spending history, often including alternative data sources in its determination.
Due to the perpetual threat of nefarious behavior and the lengths that people will go to steal money, there may never be a perfect form of fraud detection. However, being able to consider a rolling history of personal behavioral analytics will go a long way in keeping you from getting your account locked out because you went to Target twice in the same day.
Credit Monitoring and Approval
Occasionally, you may need a line of credit to achieve your life’s goals. This could be a large purchase, such as buying a new home, or simply procuring a credit card for emergencies. For some, the process can be frustration and confusing, as the financial institutions must determine your credit-worthiness before offering funding.
Traditional methods have relied heavily on manual review processes and standard credit scoring models. Unfortunately, this information is often based on limited data sets such as loan payment history and credit card utilization. Not to mention, small, innocuous events such as missing a payment or closing an account can have a significant impact on the score used to determine your approval.
AI, on the other hand, offers the ability to analyze non-conventional data sets. This may include rent and utility payments, online shopping behavior, social media profiles, and even educational background (source).
This type of consideration is especially important for underserved communities and a world that is consistently moving away from traditional sources of income and into a more freelance, gig-economy marketplace.
Conclusion
As we are firmly seated at the cusp of a new financial revolution, it is important to note that this is only the beginning. AI tools are becoming more sophisticated and ubiquitous, which will serve as a catalyst for rapid growth in all industries.
The initial reaction to removing the heavy human element of financial management is understandably cautious. Job displacement, unfair decision-making through accrued biases, and privacy concerns are only the tip of the anxiety iceberg, and these concerns have to be appeased.
However, when it comes to managing wealth, there is a very low threshold for error. People appreciate round-the-clock attention and an automated system trained specifically for their preferences and goals.
While we may view the transition as initially impersonal, it would appear that the system has become more personal by the minute.
Gone may be the days of screaming brokers and unbalanced checkbooks, but the days of a computer knowing you better than you know yourself are here to stay.