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The automation of repetitive tasks is among any organisation’s goals, as it is the remedy for low productivity. But how to achieve it? Well, there are numerous technologies that you could use for this sake, one of which is machine learning. How does it help in this case? Find it out in this article!
Table of Contents
- Automation of Repetitive Tasks with Machine Learning: Use Cases
- 1. Automated Customer Support
- 2. Compliance, eKYC and Anti-Fraud Protection
- 3. Document Processing
- 4. Improving Existing Systems
- The Takeaway
Automation of Repetitive Tasks with Machine Learning: Use Cases
If you’re considering machine learning services for finance to automate the repetitive tasks in your organisation, you’re making the right decision—ML can do wonders when it comes to automation and boosting productivity. To show you how powerful it is, we prepared several examples of this technology put into 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 teach your banking chatbots and automate (partially) your customer service. How does it work?
Many of your customer support tickets are quite repetitive and simple to solve—they are a waste of time for your tellers and customer support agents, who could work on more complex cases instead. With ML, you can embrace automation and take those repetitive tasks off your staff; all you have to do is feed the model with relevant information, deploy your chatbot and tune it for high performance & accuracy..
Naturally, you’ll need to ask the users to select their ticket type first, so that the system can decide whether the user should talk to a chatbot or a human agent. Nevertheless, this way you can save hours of your customer service time and put it to a much better use.
Embrace automated solutions. See our digital process automation services.
2. Compliance, eKYC and Anti-Fraud Protection
Another example of repetitive task automation possible with AI involves your anti-fraud measures. Here, you feed the ML model with historical data to teach it what are the patterns indicating fraud or money laundering. Then, you deploy such a model to analyse your clients’ transactions.
As a result, you don’t need experts overlooking all the transactions and user operations manually; instead, your team can focus on the transactions and clients flagged by the system—situations in which there is a true risk of wrongdoing. The same goes for client authentication in eKYC processes.
3. Document Processing
Isn’t it a waste of time that your documentation specialists spend hours just inputting data from unstructured documents into the system? Well, this no longer has to be a problem. With the help of ML and other technologies (e.g., OCR), you can design a system that will automatically scan unstructured documents and extract information from them.
If you want to learn more about this, read our article on how banks are using AI-driven document processing.
4. Improving Existing Systems
You may also use machine learning to improve current repetitive task automation. This is perfectly illustrated in one of our case studies: Retail banking superchargerd with AI – improving transaction classification.
The said bank used a rule-based customer segmentation module which could unsuccessfully categorise up to 50% of customers. To battle that, we (among others) developed an ML model and improvements to the way the data is collected. The result? 92% of customer transactions were correctly qualified via the automated system.
The Takeaway
As you can see, there are many ways how you can improve the automation of repetitive tasks in your organisation using machine learning. The above examples are just an essence—there are many more opportunities that you can open using ML, so don’t be afraid to test this technology. If you need any help, contact us at Ailleron; we will develop the perfect solution for you!
You might also read: The Might of Machine Learning: Opportunities and Challenges in Banking
