How do you improve your digital banking app? Data is a key resource in this regard. Through data enrichment and subsequent analytics, banks can gain a deeper understanding of their customers, optimize their operations and come up with new opportunities for their customers. In this article, we will focus on leveraging the underlying data from MCC codes and enriching it. This knowledge is one of the pillars of the success of neobanks like bunq or Revolut, so don’t be left behind.
Let’s start with the definition and meaning of data enrichment.
Enriching payment data may seem like a very simple task, but it’s quite the opposite! In this process, we extract and integrate a great deal of information and individual attributes into datasets. Based on this data, the bank can perform analytics that allows it to enhance its knowledge of its customers, individual transactions, and geolocation, and help contribute to their needs. (For example, geolocation lets us know where the best ATM location is, etc.)
In the image below you can see the structure of the enriched data.
MCCs are standard codes used in the payment processing industry, specifically by merchants, banks, and credit card companies. This four-digit number is used to categorize the goods, services, and types of transactions that businesses regularly conduct.
E.g., if you sell clothing online, your MCC may be classified as 5691 (men’s and women’s clothing stores). Using this code, your payment processor will know how to classify, categorize and process the transaction for accounting purposes.
The MCC code will help them get an overview of their credit card transactions when they view them retrospectively, so they don’t have to search their pockets in vain for receipts.
Although the standard for MCC is defined in ISO 18245, each acquirer may categorize them differently. That’s why most banking houses choose specialist firms so that they don’t have to have a team constantly updating MCC codes and other data. Below is a sample of some of the codes (Code – Description):
When comparing merchant categorization based on MCC alone with a more comprehensive categorization (taking into account more factors), we found that only 63% of all transactions were correctly matched based on the MCC code. So if you use only MCC codes, one in three transactions will end up in the wrong category or in no category at all. This does not deliver the promised benefit to clients. Imagine you are putting together a family budget and you want to decide what to focus on based on the categories. Unfortunately, with banks that only use MCC codes you only see 2/3 of the transactions correctly. What would such a decision look like then?
Merchant Category Codes (MCC) provide a basic categorization that is recognized worldwide. However, with innovative solutions like TapiX, you are able to extract much more information from your payment data. TapiX offers a much more specific and accurate categorization with 25 main categories at the merchant level.
This allows for greater detailed differentiation between various types of merchants and services, providing a more comprehensive and accurate view of transactions. TapiX, therefore, allows you to better understand the nature and character of individual transactions and make the most of the available payment data.
Geolocation: TapiX works with Google Place ID, which allows you to garner a large amount of additional information such as customer reviews, and opening hours. On top of that, it can also provide the bank with data points about the geolocation of the payment. This is information about where the customer lives, where they work, but also where they go regularly to chat.
Categorization: TapiX provides 25 merchant-level categories and over 500 store-level tags. This categorization helps accurately place merchants into the right segments and makes it easy to filter and search for transactions. From the bank’s perspective, this is how TapiX can help identify the customer’s lifestyle.
Say for example the customer often buys kebabs at 3 am. He’s probably returning home from a party. This could be considered ‘risky’ behavior by some insurtechs. For customers who do not participate in such “risky” behaviors would receive a discount on their policy.
Another example is life event prediction. If a customer shops at a toy or baby store, they are likely to have a baby. So the bank can offer a baby savings account as a cross-sell.
ATM network optimization: thanks to shopping data, TapiX will help you design where ATMs are missing or, conversely, where ATMs are less busy.
Data enrichment plays a key role in digital banking and provides banks with valuable information for detailed analysis. With analytics, banks can improve their services and competitiveness. Through the process of data enrichment and subsequent analysis, banks can gain a better understanding of their customers, identify new opportunities for cross-selling or conversely detect a risky customer.
TapiX, as an innovative solution, delivers data points that can be captured beyond payment terminals and adds advanced merchant categorization and enriched data, including exact merchant names, logos, categories, locations, and other relevant data.
TapiX is API-based service with 220 000+ merchant data coverage globally and 99.99% accuracy. Insert your own inputs and see the quality of the enriched data for yourself in the demo.