How Do Credit Card Companies Use Data?

Credit card companies are some of the most active data users in the entire corporate marketplace. Credit cards utilize a vast network of data analysis products, and as a result, there is a huge analytical process that goes into approving a new credit application, raising a credit limit, or assigning an interest rate on a new credit account for a consumer.

One approach that these brands use is prescriptive analytics. Many people seek out a simple way to define prescriptive analytics, thinking that this approach to data analysis is vast and challenging to understand. The truth is that prescriptive analysis is a straightforward process and one that commands a great volume of data products and algorithmic exploration. Prescriptive analytics helps brands decide what they should do rather than focusing on building models of what has already happened (or working to answer questions about the “why” of a thing). Prescriptive analysis is therefore a helpful analytical approach to credit decision-making.

Credit card providers want to develop an understanding of the ways in which the average user leverages their available credit, alongside as comprehensive a survey as possible of their unique credit habits and histories. With these pieces of information in place, a lender is able to make a credit decision with greater confidence, knowing that a user is likely to remain a credible borrower (or in some instances, unlikely to represent a worthwhile investment). Continue reading to learn more about the data collection and analysis processes that card issuers employ to make smarter decisions throughout the lending business landscape.

Credit card companies lean on data to understand profit margins.

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In addition to the use of data metrics and ongoing analysis products to develop models of user statistics, card issuers also leverage data that’s scraped from credit card machines and other merchant sources as well. Credit card machines are the devices that enable consumers to pay for goods and services at merchant outlets with ease. Instead of forcing the marketplace to rely on cash, new innovations in the credit card utilization space mean that merchants and consumers alike can take advantage of low fees (zero fees in the vast majority of consumer use cases) while selecting their preferred payment method, of which a credit card is a great option for many.

Merchants and credit card companies factor these opportunities into their ongoing business planning phases. Credit card issuers take a percentage of sales as a fee for the use of their card products, and this factors into the ongoing profit calculation that underpins the viability of credit card providers’ service models. With the absolutely huge number of consumers who utilize credit cards and credit accounts on a daily basis, this data modeling has quickly developed into an exercise in big data analytics from the utilization statistics to ongoing credit score analysis of individual users and larger population models.

Data modeling often revolves around understanding the ways in which users rely on their credit lines to provide better services.

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Services that are offered by credit card companies vary quite a bit across the marketplace. Included bonus offers are often unique across the space, but some of the core products that creditors offer would remain virtually identical if not for the analytical processes that make for customizations of offers for individual borrowers. While each credit card company might offer a basic balance transfer option, the use of machine learning and data modeling focused on the financial services space helps creditors identify pricing points that make sense for their users as a whole and individually.

Financial services brands lean on data analysis just like those in any other industrial space.