Machine learning is often associated with artificial intelligence, as these two work together to create complex, effective systems. In fintech, machine learning is used to analyze data and train AI-driven systems, improving fraud detection and customer service, creating smart pricing lists, forecasting cash flows, or even maintaining the security of the customers. Here, we shall explore this topic more thoroughly – we invite you to read on!
Machine Learning in Fintech: Use Cases
Let’s start with how and when machine learning is used in fintech.
Pricing
Firstly, you can see machine learning utilized to create smart prices – ones that are adjusted to the needs and price sensitivity of the customer. This way, financial institutions can maximize their profits while maintaining high sales.
Smart Alerts
Another use case of machine learning in fintech can be observed in creating more comfortable customer journeys, namely through smart alerts. ML is used here to analyze and understand the recurring expenses of the customers and provide them with smart alerts about upcoming transactions.
Churn Prevention
It is also possible to utilize ML to reduce customer churn. Here, the case is simple: AI is fed with historical and current data through machine learning and hence learns what are the indicators of churn. Then, the system uses this knowledge to mark customers who are close to leaving the institution, making it possible to introduce measures encouraging the customer to stay loyal.
The same logic and systems can be utilized for other processes, like cross-selling or upselling.
Security
Machine learning is also employed in fintech to improve customer security. Systems trained through machine learning are capable of detecting anomalies in customers’ transactions, thus enabling you to stop such suspicious transactions and prevent the customer from being the victim of theft.
This can also be used for general fraud detection and locating indicators of money laundering.
AI Chatbots
It’s also possible to train intelligent chatbots via machine learning, hence improving customer service – both for the customer and for your institution. Such systems are, after all, more accurate and provide your clients with better support. On the other hand, they also contribute to lowering your operational costs by decreasing the need for human agents.
You need to remember, though, that such systems still require human monitoring.
The Role of Machine Learning in Fintech
As you can see, the role of machine learning in fintech is quite straightforward – it helps build better systems, ones that optimize processes within financial institutions but also contribute to higher customer satisfaction and loyalty. Hence, you should introduce machine learning (and AI) in your institution. But, is it really that easy?
The truth is that, although machine learning plays a crucial role in fintech, you have to know where and how to implement it. For that, you might consider ML consulting services with expert agencies responsible for preparing such tailor-made solutions. This will help you understand what your institution needs machine learning for. After all, machine learning will have a powerful impact on your bank only if it’s implemented in the right places.
The Takeaway
Machine learning in fintech contributes to improvements in almost all areas of banking. Therefore, we recommend implementing it into your systems. To do that effectively, consult professional organizations handling and developing AI and ML solutions – they will be able to guide you through the implementation and help you locate the areas that need machine learning the most.
