Data Analytics Partner: FIS

Rahul Kumthekar
Rahul Kumthekar, FIS

Data has always been at the heart of central banks’ work and, over time, it is only growing in importance. The Covid-19 pandemic highlighted how important it was for central banks to have access to the most timely data possible, and to have the tools to respond flexibly to changing data needs.

In this context, FIS – with its Fame data platform – has been a trusted partner to central banks and statistical services for more than three decades. The platform supports data warehousing, business intelligence tools and statistical data exchange portals, and offers built-in analytical capabilities. FIS’s team is well-versed in integrating the platform with central banks’ existing systems.

Rahul Kumthekar, a director at FIS, says the platform comes with more than 300 functions built into the database itself. Many central banks also develop a library of their own to suit their particular modelling needs. Fame comes with its own fourth-generation programming language, which “anyone can pick up”, says Kumthekar. “It is quite powerful, you get full control.”

The platform is optimised for time-series analysis, and helps central banks match up datasets that might be at different frequencies. “Time scaling is one of the most powerful features,” says Kumthekar. Having the analytics built into the platform allows data to be analysed “on the fly”. Users can aggregate data quickly, run functions and then disseminate the data in a range of formats. Application programming interfaces let users tap into other databases.

FIS counts major central banks as its customers, including the Bank of England, Bank Indonesia and the Bank of Spain. Clients speak highly of the company’s support team, which constantly rolls out upgrades to the system as users’ needs change. Recent examples include audit trails and integration with other systems, such the R programming language or business intelligence tools such as Tableau.

Clients also mention the efficiency of the platform, which can handle millions of separate data series and transform them as needed at great speed. Kumthekar explains this is supported by the object-oriented analytical database. Treating data as objects rather than structuring it into a relational database yields significant speed gains while taking up less hard-drive space. Where outputs are needed in other formats, Fame provides this – from Basel III monitoring reports to statistical data and metadata exchange.

One thing that stands out is the length of relationships FIS has maintained with its central bank clients. This seems likely to continue, even as central banks look to build a new generation of data architecture to handle challenges on the horizon – from big data to cloud integration.

One client at a central bank with a long relationship with FIS says they expect the platform to remain a part of the data architecture for years to come.

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