Predict, Prescribe, Prosper: Unlocking the power of data and AI in public sector banks
The Data Dividend for India's Banks
India's digital economy is witnessing unprecedented momentum. Fueled by government initiatives such as Digital India, Jan Dhan-Aadhaar-Mobile (JAM) trinity, and the rapid adoption of UPI, the country has become a fertile ground for digital transformation. Digital payments are growing at a compound annual growth rate (CAGR) of over 30%, with more than 17 billion UPI transactions monthly as in April 2025. As mobile penetration deepens and consumer behavior shifts online, banks are accumulating vast volumes of data—structured, unstructured, and semi-structured—across every touchpoint.
For India's public sector banks (PSBs), this explosion of data presents both a challenge and a transformative opportunity. From transactional records and scanned documents to voice logs, app interactions, and behavioral insights, the data generated holds immense potential. One of the key focus areas due to this enhanced digitalization is SME lending, whose growing digital footprints across GST, banking, and transaction systems now make it feasible to assess creditworthiness with far greater speed and precision.
In this scenario, traditional analytics models—centered on retrospective dashboards and regulatory reporting—are no longer sufficient. To remain competitive and future-ready, PSBs must adopt analytics at scale - not just for efficiency, but to unlock new growth areas such as SMB lending models, risk adjusted cross-sell models, personalisation, etc. They must harness advanced analytics and artificial intelligence (AI) to predict, prescribe, and prosper.
Why Traditional Analytics Is No Longer Enough
Traditional data analytics has largely been backward-looking—offering summaries of what happened and why. While useful for compliance and strategic reporting, it fails to deliver the predictive foresight or real-time intelligence needed in today's hyper-digital banking environment. For Public sector banks, traditional analytics has largely centered around analysing historical data to generate regulatory reports, MIS summaries, and support strategic planning. These approaches, while valuable, often focus on retrospective insights — what happened, when, and why — without offering foresight or actionable intelligence.However, banking today is no longer willing to wait. This is particularly relevant in areas like SMB and retail lending, where expectations around turnaround time, credit personalization, and digital servicing continue to rise.
Three forces are accelerating the shift toward advanced analytics in PSBs:
Demand for Real-Time Responsiveness
Need for Hyper-Personalization
Urgency to Modernize Legacy Data Platforms Laying the Foundation: The Role of a Modern Data Platform
Advanced analytics models—be it predictive credit scoring, risk monitoring or generative AI —are only as powerful as the data they ingest. For PSBs to enable smarter lending they must invest in a modern, cloud-ready data platform that offers- Centralized data ingestion from core banking, GST systems, Bureau reports, CRM, digital channels, and other relevant ecosystem partners- In-built data governance, lineage tracking, and quality assurance- Scalable, real-time processing for analytics and AI workloads- Secure, compliant architecture aligned with RBI and sectoral mandatesThis platform is not just infrastructure—it is the strategic enabler for unlocking value across the banking value chain.
Persistent hurdles impeding advanced analytics adoption
Poor data quality and fragmented views of customer and business accounts
Siloed infrastructure that separates operational and transactional insights
Limited integration of alternative data (e.g., GST, digital payments, etc)
Shortage of skilled data science and engineering talent to translate models into production outcomes Key enablers for PSBs to adopt advanced analytics
Successfully adopting advanced analytics in public sector banks (PSBs) requires more than just selecting the right technology. It demands strategic alignment, modernized architecture, and organizational readiness across multiple dimensions. Based on common implementation challenges and proven remediation strategies, a practical readiness framework can be anchored around the following pillars:
Define a Business-Aligned Analytics Strategy
Start with high-impact use cases like fraud detection, credit scoring, and churn prediction to demonstrate early value and secure stakeholder buy-in.
Develop Organizational Capabilities and Culture
Establish analytics Centers of Excellence (CoEs) to institutionalize best practices, drive talent development, and foster a data-driven culture where decisions are guided by insight rather than intuition.
Build Scalable Data Products
Avoid suboptimal tech stacks and quick-fix solutions that create long-term technical debt. Also ensure interoperability by choosing platforms that support open APIs and middleware, allowing seamless integration across systems.
Strengthen Data Governance and Quality
Ensure trusted, consistent data through centralized dictionaries, role-based access, and robust metadata management frameworks.
Build a Resilient Data platform and Architecture (Lakehouse)
Modernize legacy systems by adopting cloud-native platforms with real-time processing and strong data governance. Build scalable, high-performing data lakes to enable reliable, large-scale analytics
Embed Security, Compliance & Risk by Design
Integrate privacy, consent, and localization controls early. Ensure full alignment with RBI and sectoral regulations when working with cloud or external vendors.
While public sector banks already operate on scalable data platforms, evolving these architectures to support advanced analytics capabilities is essential.
Tangible Impact: Use Cases Driving Value Today
This layered view illustrates how core banking functions across retail, consumer and SMB lending, cards, commercial, and enterprise operations can be elevated through Business Intelligence, Advanced Analytics, and Generative AI. Each horizontal enabler brings progressively deeper insights — from automated reporting to predictive decision-making and AI-driven personalization. Together, they form a unified intelligence fabric to accelerate data-driven transformation across the bank.
Advanced analytics is not futuristic—it's already delivering real results across the banking enterprise:
Cost Efficiency & Compliance
30–40% reduction in compliance costs via automation Real-time anomaly detection reduces riskStreamlined documentation and automated credit memo generation in a smarter lending context specially for retail and SMB loansLower processing costs for small-ticket loans through digital workflows
Top-Line Growth
Smarter underwriting improves credit quality Early warning systems mitigate NPA risksPersonalized offers boost conversionsCash flow–based scoring improves credit reach to thin-file SMBs
Enhanced Customer Experience
Faster loan decisions Intelligent product recommendations24/7 self-service analyticsDigital-first onboarding journeys for SMB borrowersAI-powered chat support for loan status, eligibility, and documentation assistance
Conclusion In the digital age, data is the new core capital. For public sector banks, embracing advanced analytics at scale is not just a technology upgrade—it is a strategic imperative. By investing in scalable data infrastructure, aligning analytics with business priorities, and fostering a data-driven culture, PSBs can unlock a virtuous cycle of innovation and impact.
The authors are Dibyanshu Lahiri, Director, BCG; Shray Jain, Director, BCG; Vipul Singh, Lead IT Architect, BCG and Nishchal Pawar, Senior IT Architect, BCG.
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