Machine learning, large language models, artificial intelligence—all of these are more and more commonly used in financial services. This isn’t surprising; after all, these technologies open a large box of new opportunities for commercial banks, insurance companies, wealth management firms, and other organizations operating in finance. In this article, we look at the first tech from the list and its practical applications. Do you want to learn more? Then keep reading!
Financial Services and Machine Learning—Our 4 Use Cases
So, without any further ado, let’s take a look at the
practical side of machine learning in financial services. How can you use this technology to your
advantage?
Process Automation
Perhaps the most basic application of machine learning
in finance is process automation. Designing a language model and
feeding it with your historical data will enable you to automate a plethora of
different areas in your organization. This is why this application is not only
the most common but also the most important.
What can you automate with the help of ML? Here are a
few examples:
- transaction classification,
- fraud detection,
- credit score and risk calculation,
- product and content recommendations,
- investment strategies and planning.
Naturally, the above examples are only the tip of the
iceberg—with the right tools to aid machine learning, you can hyperautomate
many more areas of your financial services.
Building Chatbots and AI Assistants
Chatbots were introduced to banking a long time ago,
but with the rise of generative AI, they have observed their own rapid
development. Nowadays, you can utilize them not only for the sake of automating
communication with the clients but also to streamline the work of your tellers.
How does this work?
You can feed your machine learning model with your
internal documentation to create a smart search engine for your
agents. This way, all they have to do is write the right prompt, and
they can find information much faster. As a result, they spend less time on
each of their cases and are able to process more of them each day.
Data Security
Another key use case of machine learning in financial
services is for the sake of… data security. You can use your data about
previous cyber attacks and any threat intelligence your team has gathered to
create an automated risk-detecting model—one that will inform your
cybersecurity team about potential attacks in advance.
Naturally, such a model won’t be 100% accurate,
but it will help detect threats faster and act more proactively. Therefore, it
will visibly increase your level of data security.
Algorithmic Trading
Finally, machine learning is utilized in wealth
management for algorithmic trading. The process itself is pretty simple—instead
of analyzing the market manually, your ML-powered system locates investment
opportunities automatically, selecting the best assets using historical and
current market data.
The Takeaway
Financial services providers utilize machine learning for various purposes, as it opens the door to better, faster, and more
profitable services. Thus, our conclusion here is simple: it is high
time to invest in and unleash the power of ML in your organization!
