Can Smaller Banks Lead the Way with Analytics?

Real-time data solutions enhance risk-based analytics to improve digital lending

Big and small Banks have digitalized extensively to deal with the pandemic. Especially, for customer experience and engagement. AI-powered solutions like chatbots, cognitive routing, and smart search are significant digital improvements. Here, data systems are at the core of it all.


One big question is – which systems should Banks improve first? Tech. stacks for both big and small Banks have different benefits for their customers. To enhance strategy for customer retention and acquisition, tech. stacks should enable Banks to sustain in the evolving digital sphere.


Some technologies are helping smaller banks thrive. Digitalization has helped fortify market share where top Banks in India pose a threat. As analytics evolves, upgraded systems should help smaller players build on their ace – consumer trust.

Small Banks' Long-Standing Relations v/s Big Banks' Lucrative Market Offerings



In the digital-first era, the tables have turned to prioritize sustainability. Predicting a customer’s growth is more important than just having good products for quick lending. Smaller banks may have aligned their offerings to suit their customer base, but they'll lose out on customer acquisition and retention if they miss the bigger picture.


Top banks in India will undoubtedly offer monumentally better services to the same customer base. One significant disadvantage for them is that smaller banks are trusted more, and enjoy mindshare earned over the years of providing dedicated services.


Here, newer players are yet to win customer trust in quite a few niches. Moreover, with the increasing number of digital services by Fintech Lending Companies, there are no more barriers to the entry of bigger banks.


Digital literacy is catching on gradually and trusted smaller banks can leverage this window by using analytics decisively. For example, in rural farming, difficulties in borrowing are prevalent, but these will decrease as agri-tech takes the center stage.


With India at forefront of development, competition for a number of segments is expected to intensify sooner than later. Smaller banks need to make informed decisions to extend a helping hand — digitally.


Rural Segment: What Digital Maneuvers do Smaller Banks Need?


Top Banks in India are improving the customer experience for urban customers through the personalization of services. For smaller players, this is easily achievable with their consumer base and can be introduced with cost-friendly, mobile-first API ecosystems.


But for the fast-growing rural and other niche segments, personalization requires –


  • Real-time Data Analysis: Continuous re-calibration of risk models based on new data to reflect current market scenarios

  • Data System Modernization: AI/ML-powered systems for quickly-delivered, actionable insights from predictive analytics

  • Hybrid Customer Service: A flexible workforce to help with virtual assistance that helps improve customer experience

The wave of digitalization that has coerced smaller banks to innovate is the same wave forcing top Banks in India and fintech companies to collaborate with each other. In the case of rural segments, top Banks in India are not far behind. They are aggressively partnering with agri-tech companies dedicatedly working on empowering rural communities in India.


How Real-time Data for Analytics is Helping Small Banks Lead Better?


If smaller banks want to capitalize during this initial phase of digitalization, a few analytical systems that mirror bigger banks’ work ecosystems have to be put in place. Majorly, the required real-time Banking Data systems are easily adaptable through Software-as-a-service (SaaS Subscription) offerings –


Supervision of a Banks Risk Portfolio – Assessment of a bank’s risk profiles for timely and accurate data reporting for RBI reports and business analytics.


Fraud Detection and Prevention – Predictive analytics to pick up on minute differences in transactions and determine their legitimacy in a quick, unquestionable manner.


Managing Credit Card and Loan Default Risk – Predictive analytics for possible defaults by credit card holders and loan debtors, and roadmaps for customer-friendly collection.


Unsurprisingly, customer lifecycle analysis can be sourced from insights on customer behavior monitored in the lending process. The analytics involved require Banks to explore various angles to look at data coming in from both basic asset classification and client relationship management solutions —


Modeling Customer Lifetime Value – Predictive Analytics for the net profit attributed to the entire future relationship of customers with a bank for offering value-adding services.


Newest Intuitive Marketing Modules – Predictive analytics in journey mapping and hyper-personalization for customers in borrowing, debt collection, transacting, and marketing.