Financial services use machine learning for various purposes, but there’s one certain thing: it’s a technology that revolutionises many organisations. What can it be used for? Find it all out in this article!
The Applications of Machine Learning in Financial Services
So, how is machine learning used in financial services? Let’s examine several examples that portray the efficiency and effectiveness of this technology.
Customer Service and Experience
Customer service and experience teams utilise machine learning mainly in their analytics systems. With this tech, it becomes possible to use historical data for the sake of the present and the future, predicting certain customer behaviours and proactively approaching them.
The most common example is customer churn. Do customers leave banks without any indicators of them doing so? No, there are patterns to that, yet we, as humans, are incapable of recognising them. An AI system powered by machine learning, on the other hand, can and will see such patterns and inform your team that the customer might make the decision to leave your bank. This way, your employees can introduce measures that will shift the client’s decision, and you’ll achieve higher customer retention rates.
Document Processing
Machine learning may also be implemented for the sake of document processing. Modern systems based on it can extract unstructured data from paper documents and then input it into your systems to accelerate the process and reduce error rates. What if the writing is unintelligible? The system alerts your employees, who can input the information manually!
Customer Segmentation
It’s also possible to use machine learning to segment customers in financial services. Basic automated systems make a lot of errors; hence, they cannot handle the complexity of transactional data. Those based on ML, on the other hand, are much better, with the best solutions reaching over 99% accuracy!
Fraud Detection and Prevention
Finally, there is fraud detection and prevention. Machine learning may be applied to this field of financial services to automate the process of flagging potentially fraudulent transactions based on historical data. What is the result of this?
You will notice transactions that might indicate fraud and that wouldn’t be spotted by humans (similar to the customer service example). So, your bank will be much more secure, and you should avoid potential fees related to AML and KYC.
The Benefits of Using Machine Learning in Financial Services
We’ve shown you some examples; now, let’s focus on the gains from using ML in finance.
- Higher efficiency—machine learning is key for more advanced automation, which improves the productivity in your organisation.
- Customer satisfaction—knowing more about your customers allows you to react to their changing needs; thus, you can deliver better customer experiences.
- Cost reduction—without errors and with higher productivity, your bank becomes more profitable as the operating costs are decreased.
The Takeaway
Machine learning is revolutionising financial services. Thus, if you haven’t implemented it yet, be sure to invest in ML. It will pay off in a matter of months.
