The ground that fintech companies have been gaining is vast and much of this is due to machine learning, as part of all the innovation that these types of companies offer to the financial environment.

By the end of 2021, it was estimated that the number of fintech companies in the Americas was greater than 10,000, representing an increase of more than 80% compared to 2017. In addition, around 2,300 of these companies were from Latin America, making said region a market with high development potential.

To this we must add that fintech companies in Mexico have an important projection for their growth in the coming years, especially due to the increase in demand for digital banking services by the new generations.

Machine learning and its importance to consolidate fintech

In order to achieve these goals, these services must become more efficient in the way they operate, and rely on technologies that make them more sophisticated in their financial operations.

What is machine learning?

It is a system that uses artificial intelligence to learn, based on data management, that is, from its own operation, instead of doing it thanks to programming.

For one of these models to go live, the first thing that is done is to train its algorithm with data, then it is given an input for real data, and based on the process, the system continues to continuously learn, improving itself. and taking into account the information it receives constantly.

Why is it beneficial for fintech companies?

Due to its operating characteristics, machine learning can keep pace with an economy based on Open Banking, with greater and better data management and more digitized, not only in the fintech landscape in Mexico, but throughout the world.

Some of its utilities in financial operations for companies and individuals are:

Predictive analytics

Because you act on a lot of information, you can use historical data and statistics to forecast future scenarios. In this way, fintech companies can anticipate situations that are not favorable to them and create routes to their objectives.

Higher productivity

Thanks to the fact that it works in real time with large amounts of data, machine learning can analyze and identify patterns very quickly and efficiently, optimizing times and promoting greater performance of resources.

Detection of possible fraud

With so many transactions analyzed, this system can detect movements that indicate the possibility of someone wanting to commit fraud, which is very useful to avoid losses for financial companies.

Selection of relevant content for the user

One of the attractions for fintech users is the fact of receiving product proposals tailored to their needs and according to their interests. This is also achieved with machine learning, since it identifies trends, needs, and tailors personalized services that are very likely to interest customers.

Improve decision making

All of the above converges on this. Machine learning helps financial institutions make better decisions, based on thousands of pieces of data and their objective management.