Banks have successfully evolved from a store of value to a store of information: Muraleedhar Ramapai, ED-Maveric Systems

In a rapidly evolving financial landscape, where technology is reshaping the banking industry, data analytics has emerged as a transformative force. We had the privilege of doing an interview with Muraleedhar Ramapai, the Executive Director of Maveric Systems Ltd., to delve into the profound impact of data analytics on the banking sector.

Some edited excerpts:

How are banks leveraging data analytics to build personalized solutions for customers?
Banks and Financial institutions are leveraging data analytics to deliver hyper-personalized customer experiences. It is being leveraged across the customer lifecycle to contextualize their journey than providing a mass one-size-fits-all experience. Some of the key stages are:

i. Customer Acquisition: Combining internal and external data
a. For example, internal data could include marketing data, transaction history, interaction with the bank across multiple touchpoints, their existing relationship and history of relationships, third-party information from data providers like bureaus, credit rating agencies, and social media data – super personalized as a segment of one.
b. Similar data points can be used to build offer models, underwriting models, and better pricing decisions by simulating various scenarios, picking up better target markets, and setting up internal targets.

ii. Extremely personalized relationships by knowing the customer intimately through the inputs the customer provides through multiple touchpoints – IVR, emails, calls, branch visits, ATM visits, mobile apps, and their expressions on social media. It is possible to predict why the customer is calling and be better prepared with a customized solution or offer during their next call. It could also lead to deciding which expert should handle which customer in the call center to enable optimal resolution.

iii. Strategies to improve customer retention by analyzing and predicting behaviors while constantly listening to the voice of the customer.

iv. Reduce entire decision process time, especially for traditionally time-consuming transactions like loan products, by building data science models using past transactions, emerging economic and competitive scenarios, third-party and government agencies, and the competitive landscape and emerging asset prices.

v. Product development by analyzing and listening to the customer across various forums, including internal CRM IVR website, emails, branch visits, interactions on other external avenues like Play Store and App Store reviews, social media posts, and activities.

What are some of the digital initiatives that Maveric Systems have taken internally, and what has been its impact?
i. Maveric has created a framework to analyze customer reviews from multiple channels and create a better customer experience called VoCAL (https://maveric-systems.com/brochures/vocal-app-analytics/ ). It can help banks ensure that interactions through their various channels are continuously improving.
ii. Customer 360 framework for banks using which banks can collate data, make sense of the data, segment, slice-and-dice the transactions, all of which enable personalization.
iii. Open Banking Suite which can bolt on to multiple core banking systems and make the journey smooth and reliable for the customers. One critical area for customization to enhance CSAT is enabling them to use the bank’s data and personal transaction data. It is possible through the Open Banking Suite.
iv. Improve integration with 3rd party providers helping improve the decision-making processes and enabling faster availability of finances for the banks’ customers.

How is Maveric Systems helping build resilient banking systems?
i. Move critical banking systems to the cloud to improve availability and scalability.
ii. Legacy modernization expertise ranges from moving core banking from the previous generation of non-scalable opaque systems to modern cloud-hosted service-oriented core banking systems. Migration services utilize ready-to-use risk metrics, ensuring incident-free service migration for the end customers.
iii. 3 Enable continued uninterrupted and scalable service by helping banks accelerate the learning curve by implementing extremely reliable SRE and DevSecOps.
iv. Test various architectural and technology choices in our digital labs to understand the sensitivity of resilience in each option.
v. Ready-to-use solutions for business continuity testing of all customer-facing applications.

What is the role of Data Analytics in managing compliance risks better?
Banks have successfully evolved from a store of value to a store of information. This evolution has been highly dynamic, and regulatory challenges are already catching up. An exponential explosion of data characterizes this evolution, and regulators have become more demanding. E.g., During the pandemic, when governmental aid was distributed through banks, banks were required to deliver regulatory reports with more details and an increased frequency of reporting. With the explosion of data and advances in data science, fraudsters have also become highly sophisticated. Regulators have responded with more insistence on online evaluation of risk. Banks traditionally were not ready to do this.

But now sophisticated data, big data technologies, and data science algorithms are available, helping combat these sophisticated fraudsters, ranging from anti-money laundering, funding of terrorist activities, or even human trafficking.
A new dimension is getting added where governments are working through banks to ensure ESG norms are being adhered to. It is yet another dimension that banks could not manage with their traditional approach.

We will soon, if not already, start seeing more regulatory reporting required in this spectrum, which includes eliminating fraudulent activities. As banks want to ensure that people doing more for energy and sustained sustenance are rewarded and the schemes land correctly, they will seek support to leverage advanced algorithms and data technologies.