BNP Paribas S.A. is a French international banking group and one of the largest banks in the world by total assets. With roots of its first founding in 1848, the current institution was formed by the merger of Banque Nationale de Paris (BNP) and Paribas in 2000, and is one of the three major international French banks.
It has both retail and investment banking operations, with the former business unit serving more than 30 million customers in its three domestic markets of France, Belgium, and Italy. It has a presence in 72 countries and operates in the western United States as Bank of the West, and in Poland as BNP Paribas Bank Polska S.A.
In the last few years, the IT team of BNP Paribas Bank Polska turned to the use of in-memory technologies to solve critical business issues that involved systems integration across merged companies, as well as architectural modernization to expand new opportunities. They are also exploring how stream processing technologies can be leveraged with hot initiatives like machine learning to further modernize their architecture.
The comprehensive IT department in the Poland division is largely self-sufficient from the rest of the parent company. The team consists of many technical experts with a variety of skill sets and responsibilities such as architecture definition, technology recommendation, standards definition, research, development, systems integration, and DevOps. They operate as an agile department to support several specific business areas in the bank: retail, corporate investment, central (core) banking, and personal finance.
In their Internet banking initiative, they were looking to leverage microservices, containers, and more DevOps resources to boost the team’s agility. At the same time, they owned very stable core systems and wanted to make sure they could integrate modern technologies for newer initiatives without impacting the legacy systems. The aim was to reconcile both core stability and new high velocity initiatives.
The team was familiar with how in-memory technologies could be used to address the bank’s needs. They were new to Hazelcast and had deeper familiarity with products from other vendors such as Redis Labs, GigaSpaces, and Oracle (Coherence). Due to limitations and pricing of the other technologies, team members experimented with Hazelcast, and the results of a proof-of-concept (POC) led to additional use of Hazelcast in their systems.