Blog Posts |

Data Monetization Case Study: A Breakthrough in Credit Cards


Katherine Mitchell

| Head of Demand Generation

August 1, 2022

In the credit card industry, we don’t value all our data. The top five credit card issuers processed 2.5 trillion dollars in transactions in 2020 alone. 

The problem? We can’t usually access or monetize it because of privacy rules and outdated approaches that trap value and don’t protect privacy fully.

What if you could implement a software tool that would allow you to fully monetize the sensitive credit card and provide provable privacy protection? One of our credit card customers — a top 5 bank — did just this by using LeapYear to analyze and manage their sensitive data. 

How can banks monetize data?

Financial institutions with access to transaction data have tremendous investment insights that could benefit their internal businesses and outside investors in alternative data. The big obstacle is privacy — much of the data simply cannot be shared because of regulatory rules, contractual obligations, and confidentiality agreements. You invested in collecting this data. But haven’t tapped its value. 

The legacy processes used by banks to protect privacy have long been considered insurmountable. In fact, the credit card customer profiled in our data monetization case study had tried to solve this problem for decades with limited success. Finally, after several unsatisfactory initiatives, they asked LeapYear:

 “How can we share more data while protecting the privacy of our customers and merchants?”

LeapYear explored the data available and created a solution that improved data access and provided greater business value while implementing provable privacy.

How can you prevent sensitive data exposure?

The mathematical standard of differential privacy underpins LeapYear’s software platform. Differential privacy identifies the sensitive data elements in a data set and ensures that those remain private while allowing access to those elements with analytical value. 

With LeapYear’s differential privacy, you can analyze data sets without revealing any sensitive information or whether a specific customer is in the data set. And with a quantifiable assurance of privacy, you set yourself apart as a leader in protecting privacy.

LeapYear’s data monetization solution is straightforward to deploy and use. It can be used by staff data scientists on your systems — on-premises or in a private or hybrid cloud — to perform privacy-protected analyses. Meanwhile, the processes unrelated to privacy remain untouched. 

Ready to make your breakthrough in data monetization? Then, read this data monetization case study

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