Artificial Intelligence is the most sought-after innovation in the financial industry after blockchain according to most experts. Based on the current situation in the fintech sphere these two “features” are the only ones that could somehow help a company stand out from the competition. However, they are quickly becoming a stable addition to the roster as well.

The only thing that is left in the differentiation of companies is the method with which the AI was implemented in the first place. How much time was dedicated to the machine learning process and how fast did the company unveil its new AI feature. All of these questions usually come from the customer side, be it a B2C or a B2B partnership.

Implementation process

In order to truly understand how AI is added as a feature in a financial company, we first need to understand what this financial company targets. Let’s take two examples for this article. One will be a trading services company and another could be a lending services company.

Trading

When we take a look at AI implementation for trading, we immediately see the difference between AI and machine learning on a much clearer basis. You see, AI is pretty much the finished product. Machine learning is the process of achieving that finished product as well as improving it once it’s ready for launch.

With trading, the machine learning process is pretty much constant. There are always new strategies, tools, and news coming out in the market, which means that the AI needs to learn as many variables as possible. It needs to draw conclusions and connections between a set of events and a price change in an asset be it stocks or currencies. Furthermore, it needs to draw the correct connections between these events. If not, then it is doomed to repeat them in the future and forcing the developers to manually remove that specific algorithm and starting the whole process again.

The learning phase for AI in the trading department is extremely tenuous because of the variables. Whenever AI is used to detect something on an existing image or footage, it’s much easier for it as it can just identify the code coating of colors, combine them together and create an image that it can then compare to the database, thus determining what it is looking at currently.

With finance, it’s much different than that. Sure a human may comprehend that the thing he or she is looking at is a chart, but what exactly this chart is may escape most people. Therefore, AI in pretty much a very special case is being designed to be more intelligent than a human in finance rather than just being faster than them to log data.

Lending

The lending sector is much easier for AI. In fact, the implementation of AI in lending itself was sometimes called unnecessary in the first place as a regular algorithm would have done the job. But in the pursuit of innovation, or in most cases a marketing campaign to stand out, many lending companies were happy to demonstrate their newest “toy”.

Basically what happens, in this case, is that a lender and a borrower register on the platform. They each indicate their requirements to either give out or receive a loan. The AI then tries to find ideal matches for both the lender and the borrower, doing it within just a fraction of a second. Plus, it is designed to bring the results down to single digits and sometimes just one, so the perfect candidate can be found. With a regular algorithm, it would most probably find multiple matching parties thus making the selection process a bit more complicated. No need to mention manual choosing at all here.

And the process itself is quite simple. The developers just let the AI run its course with matching lenders and borrowers and then either confirm or deny the accuracy of the match. The more denials the AI receives the more accurate it becomes, later on, ultimately reaching a stage where it finds 100% correct matches within just a couple of weeks of testing.

Where else can AI go?

Honestly, not many people are truly aware of the potential of AI in finance within the next decade. The most obvious direction would be the customer service department, but we already have bots for that.