How is machine learning used in financial services? It works alongside AI to provide customers with a better, custom experience, improve customer onboarding, detect (and prevent) fraud, help in portfolio management, forecast the stock market, manage risks, and structure big data. Do you want to learn more? Then, keep reading.
Table of Contents
11 Use Cases of Machine Learning in Finance
Without any further ado, let’s look at the use cases of machine learning in finance.
1. Customer Experience in Financial Services
The better the customer experience, the happier and more loyal the clients are. As simple as this equation may seem, it is extremely difficult to achieve this ultimate goal. Machine learning helps with this.
Firstly, machine learning services are a must if you want to develop effective AI systems – ones that bring detailed customer data into play. Through this, you can personalize your content and product offers to each client, hence achieving much higher satisfaction and building loyalty.
2. Customer Onboarding
The second use case of machine learning in finance is related to customer onboarding. How can you deploy it to streamline this process?
In this case, you can collect data on how users navigate your onboarding pages, whether in-app or on the web. This way, you can better understand the customers by studying thousands, if not millions, of behaviors and implementing UX, UI, and procedural improvements that will make onboarding more convenient. You may also use AI combined with machine learning to find potential bottlenecks – the stages on which potential customers are most likely to abandon the onboarding process.
You need to remember that, in this case, machine learning combined with artificial intelligence are not the remedy – they are a tool to discover what should be upgraded, but it is up to you whether and how you will react. Therefore, if you use machine learning for this purpose in your financial institution, we recommend our banking app UX design services – we will help you build the best possible user experience with our expertise and fresh point of view.
3. Portfolio Management
Whether you offer retail banking services or work with commercial institutions or wealthy customers, you may benefit from implementing machine learning and AI for portfolio management. It is simple but effective at the same time:
- The customer fills in their financial goals along with the deadlines.
- The system analyzes the market data using historical data to manage risks and make predictive insights.
- The system assigns the assets to the available investments and opportunities or provides the customer with suggestions and investment plans.
4. Fraud Detection
When it comes to machine learning and the use of AI in banking, fraud detection and prevention are among the most commonly mentioned areas. Let’s look at this from the ML point of view.
In this case, machine learning is used to train artificial intelligence. The system is fed with historical data to understand fraud attempts better and spot patterns and anomalies indicating them. Then, the system is used for monitoring transactions and alerts the specialists about any potential fraudulent activity, letting you stop any wrongdoers more effectively.
5. Stock Market Forecasting
Since machine learning is excellent at identifying patterns (and finding anomalies), it makes the perfect tool for stock market forecasting. Like in the case of fraud detection, all you have to do is train your systems with historical data. Then, you can extract predictive insights about the market and combine them with your experts’ opinions to make the best decisions.
This is especially useful if you need to act quickly on the market, since AI-ML is capable of processing the data extremely quickly – with the right model, you can gain a powerful advantage.
6. Customer Churn Prediction
Remaining in the area of analyzing patterns, machine learning is excellent at churn prediction. Almost always, there are indicators that a client is about to leave your financial institution, but human employees are often incapable of finding them, while technology is.
When combined with AI, you can even create fully automated systems – ones in which, when churn intention is detected, the customer is provided with content/offers aiming to persuade them to stay or increase their engagement. But, you may also implement ML in cooperation with humans – the system may alert your customer service agents, and they might tailor the reaction based on the information about the customer received from the system.
7. Loan/Credit Scoring
Machine learning in financial services will also improve your loan/credit scoring processes. Here, you can first use it to analyze the typical data you would normally have collected and evaluated by a human agent and then add extra information to the mix in order to determine the risks.
However, in this case, you need to remember about the threat of data bias. If the basis on which you will feed the system is subjective, this might lead to unfair decisions. Therefore, you should be wary when using machine learning/AI for this purpose and best combine it with human input.
8. Employee Churn Prediction
Your highly skilled staff is critical for your organization’s operations. Hence, it is crucial to keep your employees on board and spot any signals that they are no longer motivated or willing to change their employer.
Machine learning might also be implemented in this case. Like with customer churn prediction, it will spot patterns suggesting that your valuable employees are about to hand in their notice. It might not be as effective as for customers since its performance depends on how much data you can actually collect about your staff’s work, but it is a highly helpful tool when it comes to retaining your biggest talents.
9. Marketing
Machine learning might also impact your marketing campaigns. In this case, you can use it to understand your customers better, segment your target audience, and base your campaigns on the data. Thanks to ML, you will also know what does and does not work, hence improving the general design of your marketing materials.
10. Compliance
Compliance is key in financial institutions and you may ensure it with machine learning. You can achieve this by feeding AI with legal regulations and using the system to monitor your organization. Such a model will flag any potential violations (to be examined by a human) or even help you generate reports for regulatory agencies – for instance, generate reports of green transactions as required by the EU.
11. Structurizing Datasets and Big Data
Finally, one of the key use cases of machine learning in finance is for structuring data and big data analysis. Take, for instance, lengthy contracts – without ML, you need a human to read them thoroughly in order to get all the critical information. Machine learning, on the other hand, can automate this process and do this for you.
The same goes for big data – datasets that would be impossible to analyze even by a dedicated team of human employees. With machine learning and AI, this turns from impossible to…quick, letting you extract insights even from the most complex, extensive data sources.
Conclusions
Machine learning is widely used in financial services, as it is capable of impacting almost every area of banking. Therefore, it is crucial that you implement it into your systems to gather the most accurate insights and do so quickly. If you do not know how and where to start, do not worry – check out our machine learning and AI in finance page and see what services we can support you with!
You may also read: Big Data in the Banking and Financial Services sector: use cases and challenges
