Banks rapidly adapting Cloud Technology for Machine Learning

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Founded in 2000, Maveric Systems is a software engineering services company that works across financial platforms, banking solutions, data technologies, and regulatory systems. The firm has offices around the globe to serve their banking partners spanning across 15 countries, along with a dedicated offshore delivery and research centers in Bengaluru, Chennai, and Singapore. 

The business originally provided testing services to banks and financial institutions but, by 2012, Maveric Systems spread to other fields such as product creation, analytics, and digital banking domain architecture. Muraleedhar Ramapai, Executive Director of Data at Maveric Systems, in a recent interview, has mentioned about a few important aspects of banks adapting to cloud and open-source for machine learning.

Impact of the pandemic on the company: 

Despite the pandemic, the company has been growing, and that’s one of the most important aspects of the company. Initially, the company did the practical validation of the computerization that used to happen in the banks, and then, as soon as the packages came in, they immediately started taking up the implementation, checking, consistency, and engineering for the banks. Five years ago, they added three more organizations and are now expanding partnerships with several banks to develop and incorporate microservice-based architecture in most of the other areas of the bank. 

Data Analytics is the newest enterprise in the country and the company is helping banks switch from their conventional models to newer forms through data analytics. Analytics is a comparatively limited footprint, maybe 10-15 percent of our business, but more is data engineering. 

Need for changes in the Software during the pandemic to meet the customer’s demand: 

There is a great deal of demand in terms of microservices and related factors, as well as content development — whether it is using either of the latest digital innovations or older ones to create web-based properties. There is also a significant need for data convergence and fast transfer from business units, sites, and systems to data warehouses. 

Central banks and governments are now developing numerous consumer-friendly / SMEs-friendly digital products. Current frameworks are being set up for totally new workflows. Finally, there is a need for data campaigns to facilitate new regulatory standards around the world. 

Biggest focus areas for innovation in the banking sector: 

There is a lot of emphasis on how to lend to small and medium-sized businesses. The government focuses, particularly after COVID, on ensuring that small and medium-sized businesses return to their health. Fintechs is another subject. There is a great deal of focus in Europe on open banking in partnership with fintech innovation and open APIs. Finally, the emphasis is placed on the use of data analytics as opposed to conventional approaches, whether or not they are customer-identified. 

Main focus areas for Maveric: 

The company’s typical emphasis has been on banks where they have introduced large-scale market development projects in banking networks that are being revamped and replaced. To allow digital content is another field. they are focused on developing the infrastructure of microservices, which will enable the banks to work with open banking. 

Open banking does not mean making the fintech smartphone applications, but data convergence with the bank. They concentrate on data and analytics in two areas: regulatory and anti-money laundering and fraud. Packages and algorithms are available on how to implement and operate those analytics programs. 

Technologies are the banks looking for in terms of exploring open-source and cloud: 

There is a lot of cloud adoption in medium-sized businesses. It could be fewer in America, but in Asia and Europe, businesses are taking over the cloud. Companies are going further into either cloud storage centers, and even unstructured data is flowing into them. 

The second important aspect is moving from the conventional way the research was exchanged within the banks. There is further democratization of data and data-enabled records. 

The third is the acceptance of open source, particularly in advanced analytics spaces such as machine learning. Here several ML applications are GPU-based running activities like Computer Vision using Python-based open-source frameworks. 

Legacy systems: Banks & state-of-the-art fintech companies 

Not having a legacy gives fintech an edge for sure, but it doesn’t mean it banks have bad technologies. Banks need to guarantee certain aspects that a peer to peer lender will not do before launching a product. They don’t have to abide by the different standards. A lot of banks are rapidly modernizing and are investing in technologies, moreover, and a lot of them are still open to collaboration with Fintech. So, the comparison between FinTech’s and banks is not right. It’s all like what’s in it for the consumer, and if the customer wants a decent service, banks will have to spend significantly to modernize. 

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