How is machine learning utilised in fintech? For instance, it’s a part of the process of risk management automation. Or it may be used for fraud prevention. You’ll also see it in smart credit scoring models and AI-powered customer service tools. Do you want to learn more? Then, see our list of use cases!
Fintech & Machine Learning: How Do These Two Work Together?
Machine learning is the basis for modern systems since we use AI-powered solutions, even unknowingly, in almost every aspect of our lives. Therefore, its impact on fintech is by no means surprising. Yet, do you know how exactly this sector uses this technology? If not, take a look at the most important examples below.
Automated Risk Management
Traditional risk management requires a lot of effort and manual analysis—it’s time-consuming. Yet, machine learning for fintech helps with that.
Machine learning models analyse historical data to find patterns indicating certain behaviours or trends. When applied to your current data, they can use the extracted patterns to predict what could happen in the future, hence estimating the risk. Additionally, such systems may spot potential fraud attempts, helping you comply with the AML regulations.
What is more, all of that will be done automatically, meaning that your employees won’t need to spend time on exhaustive analyses. Naturally, a tiny bit of human input will still be necessary from time to time, but it will be a drop in the ocean when compared to traditional methods.
Smart Credit Scoring Models
Credit scoring is undoubtedly lengthy. At the same time, it’s a process that causes the most stress in your customers. This is why you should do whatever it takes to make it faster and more pleasant for the clients—and that’s exactly what’s possible with the use of machine learning.
An ML model can prepare credit risk assessments faster and more accurately than a team of human employees. It can even reduce the lending bias, though this could be treated as a controversial statement—whether it will do so depends on the quality of data the system is fed with (aka the extent to which you eliminate bias from source data).
Customer Service Solutions
When it comes to customer service, fintech and machine learning, there are many combinations that work.
For instance, ML can be used to teach an AI chatbot. You can also use machine learning to analyse customer data and create bespoke product offers. It might even be leveraged for automation purposes to accelerate customer-related procedures and hence make them happier—there are a plethora of possibilities here.
The Takeaway
Utilising machine learning in fintech is no longer an advantage—it’s a necessity for any business that wants to remain competitive. Therefore, if you still haven’t invested in such technology, we recommend doing so as soon as possible. Without it, you may fall behind the competition.
