It's no secret that when it comes to categorizing large amounts of data, banks no longer have the luxury of relying on traditional techniques. Advanced methods are now required to keep up in a digital age where information is continuously growing in size and complexity.
Think of it this way, categorizing data is when you group payment transactions into specific categories, such as groceries, rent, and utilities. For instance, you can see the transactions you've made, complete with the brand logo, description, and category, listed in your monthly account statement. This feature is valuable for digital banks as they need to know what their customer's spendings look like.
The integration of machine learning into data categorization processes is a game-changer for the banking industry, providing a more efficient, accurate, and scalable solution. By embracing this technology, banks can now streamline their operations and improve the user experience. This not only saves time and resources but also improves the way banks approach their customers, providing them with better products, all of which give banks a competitive edge in the region.
At Lune, our mission is to simplify the process of categorizing big data and empower digital banks to gain a deeper understanding of their customers. By combining our expertise in machine learning and data categorization, we offer a solution that is efficient, accurate, and scalable. This sets the stage for a more in-depth exploration of the limitless possibilities that digital banks can offer.
Our approach to data categorization involves breaking down transaction descriptions into smaller, more manageable subwords. This allows us to analyze and categorize data more accurately, avoiding the limitations of traditional methods.
To give you an example of how the enrichment process works. Imagine a client's bank statement showing a transaction description like "AlShayaaFZLLC - AED 25". It can be hard to tell what that transaction was for, right? But with our enrichment process, we can extract the actual brand name and provide a neat design that includes the brand logo, like this:
Not only does this make it easier for clients to understand their transactions, but it also categorizes the purchase under "Personal Shopping". This means that by the end of the month, clients will know what categories they've spent the most on. Financial institutions can then use this data and create unique products that their customers would like, for example a rewards program for coffee lovers. This could include exclusive coffee discounts, free coffee after a certain number of purchases, on-demand coffee services or other perks that cater to the client's spending habits
Once the transaction is broken down into smaller subwords, the attributes are then used to train our regional machine-learning model, made up of hundreds of small classifiers working in harmony. These classifiers work to minimize errors and ensure that each category is treated equally and accurately.
At Lune, we prioritize categories that matter most to our clients by utilizing specially designed metrics during the machine learning process. This way, we can offer tailored and relevant data categorization solutions that would truly help our clients grow.
Getting started with data categorization with Lune is easy. Feel free to reach out to us and we'll help you get started on your journey to more clean, accurate, and scalable data categorization. With our cutting-edge machine-learning algorithms and in-depth expertise, we'll work with you to find the best solution for your needs.