Continuing on our dissection of AI last week, let’s get more into where AI can be useful in the coming years.

There are many processes and connections that we often deal with which are too complex to be modeled on a spreadsheet, which is where AI can help by running and learning from an ongoing set of formulas. The same could be true of other fund data sources and web data sources to put something together that is more cohesive and comprehensive.

AI is like a small child

When I was at an AI in Fintech conference a couple weeks ago, they mentioned how AI is very much like a small child. What a phenomenal analogy! A child comes into this world not knowing anything and is presented masses of sensory objects and experiences that don’t have an identity. Due to the influence and knowledge base of those around them, they begin to learn things based on the items we identify, or “tag” such as an “apple”, or a “dog” which they will then begin to associate and tag themselves. It may take some time and multiple tags to decipher, but eventually due to the domain and expertise we are implanting into them, they will be able to tell the difference between a cat and dog, and eventually—after tons of learning— even between a cockapoo and a labradoodle.

The same goes for our machines. We must first have a domain and a strong base of knowledge in a particular area that we might be considered “experts” in. Once we have learned as much as we possibly can, it is our job to then insert that knowledge into the system that will then listen, react and independently act on other data sets and circumstances which interact with it. It will record each action and result for every individual circumstance in order to act on it accordingly in the future.

Areas of influence

By using technology that can listen, react and learn through AI, many areas within financial services could benefit from adding knowledge-based systems to increase awareness and efficiencies, including the need to deal with problem reflexivity in investments such as:

  • Earnest research
  • Fraud detection
  • Regulatory compliance
  • Underwriting
  • Management
  • Threat detection
  • Personal banking
  • Personal finance
  • Reporting

More than a feature set

As mentioned earlier, AI is one of those buzzwords that’s on every digital newsfeed and is mentioned by multiple vendors trying to maintain an appearance of being ahead of the curve.

Does this mean that all companies need to promote AI as part of their feature stack? Not necessarily. Because AI is in its growth phase it may be an element that users are looking for, but it will soon be in the DNA of all systems and processes. There is also a pretty good chance you are leveraging some elements in your business today.

Think of AI like 3G, 4G, 5G. At the onset, it was key to get on board with the latest and greatest, however now we no longer care about the number of “Gs” powering data and systems. We just care they are powered and working at all. AI is simply a bunch of business intelligence where data science and learning are applied on a continual basis.

Digital Disruption Regulation

Because of the rise of AI, more startups are able to create digital disruption within the financial industry by going around the slower and more regulated banks and starting from scratch, avoiding much of the red tape often found in larger companies. The main differences between the two approaches are advocacy, transparency, and freedom. We know that regulation is critical in our industry to ensure trust and level the playing field, we just need to ensure that it is in the game.

Although we may not yet be at a point where we have to worry about the security of our jobs, it would be an ignorant thing to say that AI won’t be a part of our jobs and our industry in the near future. This incredible technology is even more relevant and useful to our industry than other recent tech like social media for financial professionals, since they will be able to receive updated data and data sets in regards to current and potentially better-suited investments and performance as machines continue to learn.

If AI is not on your radar as a company and you aren’t currently thinking about how it could improve your offering to clients and prospects through elements like improved reporting, automated updates and distributions of data and insights, and user learning, you are putting yourself in a risky situation. It’s not too late, but we know how difficult it is to get things started, so start soon!

By applying AI within different areas of the business, we are putting powerful machines in the hands of consumers to find and decipher mental process allowing them to be as informed and aware of information