How Does Machine Learning in Finance Automate Repetitive Tasks?

Automating repetitive tasks is one of the goals of every organization, as it can help address low productivity. But how can you achieve it? There are many technologies that can be used for this purpose, and one of them is machine learning. How can it help? Find out in this article!

Automation of repetitive tasks with machine learning: use cases

If you’re considering machine learning services for finance to automate repetitive tasks in your organization, you’re making the right decision. ML can do wonders when it comes to automation and boosting productivity. To show you how powerful this technology can be, we’ve prepared several examples of how it is used in practice. Take a look at them below.

1. Automated customer support

The first use case is already quite common – you can use ML (and LLMs) to train your systems. You can also deploy banking chatbots and partially automate customer service. How does it work?

Many customer support requests are repetitive and relatively simple to resolve – handling them can take up valuable time that tellers and customer service agents could instead spend on more complex cases. With ML, you can automate these repetitive tasks and reduce the workload on your employees. All you need to do is provide the model with relevant information, deploy the chatbot, and fine-tune it for high performance and accuracy.

Naturally, users should first be asked to select the type of request so that the system can decide whether they should speak to a chatbot or a human agent. Nevertheless, this approach can save many hours of customer service work and allow that time to be used much more effectively.

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2. Compliance, eKYC and anti-fraud protection

Another example of repetitive task automation enabled by AI is fraud prevention. In this case, you feed an ML model with historical data to teach it which patterns may indicate fraud or money laundering. You can then deploy the model to analyze your customers’ transactions.

As a result, you no longer need experts to manually monitor every transaction and user activity. Instead, your team can focus on transactions and customers flagged by the system – situations where there is a genuine risk of wrongdoing. The same applies to customer authentication in eKYC processes.

3. Document processing

Isn’t it a waste of time for documentation specialists to spend hours entering data from unstructured documents into a system? This no longer has to be a problem. With the help of ML and other technologies, such as OCR, you can design a system that automatically scans unstructured documents and extracts information from them.

If you want to learn more about this topic, read our article on how banks use AI-powered document processing.

4. Improving existing systems

Machine learning can also be used to improve existing repetitive task automation. One of our case studies illustrates this perfectly: “Retail banking powered by AI – improving transaction classification”.

The bank in question used a rule-based customer segmentation module that failed to categorise up to 50% of transactions. To address this, we developed an ML model, among other improvements, and enhanced the way data was collected. The result? 92% of customer transactions were correctly classified by the automated system.

The takeaway

As you can see, there are many ways to improve the automation of repetitive tasks in your organization using machine learning. The examples above are just the beginning – ML opens up many more possibilities, so don’t be afraid to explore and test this technology. If you need help, contact us at Ailleron – we’ll develop the right solution for you!

Key information on automating tasks with machine learning

• Core purpose: machine learning (ML) is a key technology used in the financial sector to automate repetitive tasks, significantly increasing organizational productivity and efficiency.
• Automated customer support: ML and large language models (LLMs) are used to power banking chatbots that can handle simple, common customer support requests, freeing up human agents to focus on more complex cases.
• Compliance and anti-fraud protection: ML models are trained on historical data to automatically monitor transactions in real time and detect patterns that may indicate fraud or money laundering, allowing compliance teams to investigate only activities flagged by the system as high risk.
• Intelligent document processing: by combining ML with technologies such as optical character recognition (OCR), banks can create systems that automatically scan unstructured documents, extract key information, and enter it into core systems, eliminating time-consuming manual data entry.
• Improving existing systems: machine learning can be used to significantly improve the accuracy and performance of existing automation systems, particularly rule-based ones. For example, it can increase the accuracy of a transaction classification module from 50% to over 90%.

You might also read: “The power of machine learning: opportunities and challenges in banking”.