August 4, 2020 By Matthias Fauth 3 min read

GEVA Group chooses IBM Cloud to support its payment-as-a-service offering.

Case study: “Moving a critical service to IBM Cloud

The last several years have brought drastic increases in regulations and requirements in the banking and financial services industry. As the world becomes increasingly virtual and digital, governing bodies are working double time to make sure that financial data stays as safe as possible.

In the European Union (EU), regulations are particularly stringent. For example, the General Data Protection Regulation (GDPR) sets strict parameters around the transfer of data outside of the EU.

A move toward software-as-a-service

GEVA Group is a payment (transaction) company based in Germany. As such, we must pay close attention to current regulations. Our customers include banks, insurance companies, and financial service institutions—companies that must process sensitive financial transactions, but whose primary focus is not IT. For years, GEVA Group provided these organizations with payment solutions that ran on their internal infrastructures, thereby allowing them to focus on their core businesses.

However, GDPR and other regulations have made it increasingly difficult for our customers to maintain compliant infrastructures in the financial services sector. As a result, many of them asked us to provide our software-as-a-service (SaaS), hosted on the cloud.

Choosing a cloud vendor was easy for us. We knew from the very beginning that we would work with IBM Cloud. We have a very long history with IBM, and their strong support of the financial services industry has resulted in an excellent reputation among our customers. Furthermore, IBM Cloud technology is designed to help companies like GEVA meet our industry’s many requirements

The importance of stability

In the payment processing industry, timelines can be rather tight. Most banks have very short payment processing windows. If you miss one, you may have to wait hours or days for the next window—a situation that can have devastating effects in our fast-paced industry.

This means that the infrastructure that underlies our applications must be extremely stable. As one of the few public cloud providers with numerous data centers in Europe, including in Frankfurt, London, and Amsterdam, IBM provides us with extraordinary failover support.

A heterogenous infrastructure

The stability and security of our SaaS infrastructure are further bolstered by the IBM Cloud Bare Metal Servers we chose to support our Oracle database. Geographically dispersed bare metal servers can be set up in synchronous mode so that if the database fails on one server, another will pick up where it left off with no loss of data. Another key component of our IBM Cloud infrastructure, IBM Cloud Object Storage, facilitates data content storage for applications and backup across our worldwide data center regions. IBM Cloud Object Storage also enables us to keep all of the data exchanged between our customers and the cloud redundant. The importance of this configuration—and the reliable continuation of service—cannot be understated. Ours is a mission critical business, and any loss of data would equal time and revenue lost.

Our environment is not limited to bare metal. IBM Cloud Virtual Servers offer us a distinct advantage because they can be scaled on an as-needed basis. This makes it easy to onboard new customers, plus it easily accommodates the activity surges our company experiences at quarter-end and year-end, when we process millions of transactions per hour. 

Our customers demand the best from us and we cannot risk downtime or outages. We chose IBM as our cloud provider because we knew that we could trust them, and we are already in discussions about technology we hope to use in the future. This includes IBM Hyper Protect Services, IBM MQ on Cloud, and IBM Db2. In the coming years, we also hope to begin offering our customers instant payment capabilities, which will allow us to take advantage of IBM’s expertise in artificial intelligence and machine learning.

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