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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 centres in Bengaluru, Chennai, and Singapore.

The company initially offered testing services to banks and financial institutions but, by 2012, Maveric Systems expanded into other areas like software development, analytics and digital platform architecture for banking domain specialisation.

We connected with Muraleedhar Ramapai, Executive Director of Data at Maveric Systems to know more about innovations that banks worldwide are exploring, including data analytics and open-source machine learning frameworks. Here are the excerpts:

Analytics India Magazine: How has the company been impacted due to the ongoing pandemic?

Muraleedhar Ramapai: We have been growing despite the pandemic and that’s one of the key things. We are finding good takers from newer businesses as well as the older ones for our services, especially in software and data analytics.

Initially, we did functional validation of computerisation which used to happen in the banks, then, once the packages came in, we did their installation, testing, quality, and engineering for banks. Five years ago, we added three more businesses and are now implementation partners for a lot of banks in building microservices-based architecture and integrating it with most of the other parts of the legacy banking systems.

Data Analytics is the newest of the businesses. And within data analytics, we’re helping banks move from their traditional models to newer ways. So, analytics is comparatively a small footprint, maybe 10-15% of our business, but a larger thing is data engineering. And what we’re realising is that banks will have to redo their approach on how they have been managing data to be able to go multi-channel and move away from batch approach of data to streaming analytics approach.

AIM: What have been the biggest demands from banking customers during the pandemic in terms of software?

There is a lot of demand in terms of microservices and related aspects, and also related to content development — whether it is using any of the newer modern technology, or traditional ones to build web-facing assets. There is also a massive requirement of data integration and quick movement from across business units, sites and applications into data warehouses. Also, the central banks and governments are coming out with various consumer-friendly/ SME-friendly digital products. New systems are being set up with complete new workflows. Finally, there is a demand for data movements to support new regulatory requirements around the world.

AIM: What have been the biggest focus areas for innovation in the last year or so in the banking sector?

There is a lot of focus on how to lend to SMEs. Especially after COVID, the government is focusing on ensuring that SMEs return to their health. Another focus is fintechs. There is a lot of focus in Europe on open banking in collaboration with fintech innovation and open APIs. Finally, there is an emphasis on the use of data analytics as opposed to traditional methods, whether it is customer identification, prospect identification or using AI for KYC.

AIM: Maveric is involved in the whole technology ecosystem from IT to data analytics and software development. What are some of the main focus areas for Maveric?

Our traditional focus has been on banks where we implement large enterprise transformation programs in banking systems, which are getting upgraded and replaced. Enabling digital services is another area for us. Here we are building microservices architecture, which will help them to work with open banking.

When I’m talking about open banking, we are not creating those mobile apps for fintech, but data integration with the bank. We are focusing on data and analytics in two areas — regulatory and anti-money laundering & fraud. There are packages and algorithms on how to integrate and power such analytics systems.

AIM: In terms of exploring open-source and cloud, what kind of technologies are the banks looking for?

There is a lot of cloud adoption, at least in medium-sized companies in some geographies. It is less in America perhaps, but in Asia and Europe, companies are adopting cloud. Companies are moving more towards either cloud data warehouses, and even the non-structured data is coming into them.

The second is moving from the traditional way in which analysis was shared within the banks. There is more democratisation of data and data-enabled documents.

The third one is the adoption of open source, especially in advanced analytics space such as machine learning. Here a lot of ML systems are GPU-based running tasks like Computer Vision using open-source frameworks based on Python.

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There is immense value in using Python libraries for example, to do a machine learning model, even in regulated sectors such as banking. Banks are connecting with community support such as from academia and open-source ML products to carry innovations.

AIM: Do you also have an internal team of data scientists which help develop these models and deploy them? 

We do have a small team. We are working with one large bank to enable some of their data and analytics routes. But our work is primarily focused on enabling data scientists using engineering.

AIM: There are a lot of client systems running on legacy systems, how far do you think these banks are when you compare them to the state-of-the-art fintech companies? 

Not having legacy gives fintech an advantage for sure, but that does not mean that banks have poor technology. Banks, before releasing a product, need to ensure many things which a peer to peer lender may not. They don’t have to adhere to various norms. But we’re seeing a lot of banks quickly modernising. A lot of them are investing in technology, and a lot of them are also open to collaborating with fintech.

So the comparison of fintechs versus banks is, in my opinion, not right. It is more about what is in it for the customer and if the customer demands a good experience, banks will have to invest heavily to modernise. For example, when you look at North America, and even parts of Europe, there are still transaction systems which might be running in so-called legacy mainframes. There are many beautiful integration technologies which are available these days to upgrade banks’ digital offerings.

AIM: Are you looking to hire more talent at the moment?

Talking about talent, we have labs where our team members work on streaming data coming from stock exchanges to build signal mining from that data. We are also using social media data to advise banks on what they should build on their mobile apps, where they should focus and more. These tasks require high-end talent, and we are establishing sizable data science projects, and we are also expanding our data engineering team.

This interview was originally published on Analytics India Magazine website and is being reproduced here.

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