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Time of India
23-06-2025
- Business
- Time of India
Predict, Prescribe, Prosper: Unlocking the power of data and AI in public sector banks
Live Events Demand for Real-Time Responsiveness Need for Hyper-Personalization Urgency to Modernize Legacy Data Platforms 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 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 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 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 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 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 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 forces are accelerating the shift toward advanced analytics in PSBs: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 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:Start with high-impact use cases like fraud detection, credit scoring, and churn prediction to demonstrate early value and secure stakeholder 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 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 trusted, consistent data through centralized dictionaries, role-based access, and robust metadata management 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 analyticsIntegrate privacy, consent, and localization controls early. Ensure full alignment with RBI and sectoral regulations when working with cloud or external public sector banks already operate on scalable data platforms, evolving these architectures to support advanced analytics capabilities is 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 analytics is not futuristic—it's already delivering real results across the banking enterprise:30–40% reduction in compliance costs via automationReal-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 workflowsSmarter underwriting improves credit qualityEarly warning systems mitigate NPA risksPersonalized offers boost conversionsCash flow–based scoring improves credit reach to thin-file SMBsFaster loan decisionsIntelligent product recommendations24/7 self-service analyticsDigital-first onboarding journeys for SMB borrowersAI-powered chat support for loan status, eligibility, and documentation assistanceIn 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 authors are Dibyanshu Lahiri, Director, BCG; Shray Jain, Director, BCG; Vipul Singh, Lead IT Architect, BCG and Nishchal Pawar, Senior IT Architect, BCG.
Economic Times
23-06-2025
- Business
- Economic Times
Predict, Prescribe, Prosper: Unlocking the power of data and AI in public sector banks
iStock To remain competitive and future-ready, PSBs must adopt analytics at scale to unlock new growth areas such as SMB lending models, risk adjusted cross-sell models and personalisation. 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 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.
Hans India
05-06-2025
- Politics
- Hans India
11 years of Modi govt redefined India's development narrative: Jitendra Singh
New Delhi: With bolder decisions, futuristic reforms, and transformative governance, the Prime Minister Narendra Modi-led government has in the last 11 years redefined India's development narrative and restored public faith in the system, Dr. Jitendra Singh, Union Minister of State (Independent Charge) for Science and Technology, said on Thursday. Singh stated that initiatives like the rollout of GST to the push for Digital India, or the opening up of strategic sectors like space and atomic energy to private players, the past decade has seen the government under PM Modi prioritising long-term national interest and setting new benchmarks in decision-making. 'Each initiative has been driven by the vision of a self-reliant and globally competitive India,' he said, in a media interview. Further, by enabling the Department of Biotechnology (DBT) to foster innovation, particularly in vaccine development, genetic research, and bio-entrepreneurship, the Modi government positioned India as a rising global tech hub. The seamless integration of traditional governance goals with modern technology has been another key feature of the Modi era, the Minister noted. 'Under PM Modi's leadership, sectors like space, atomic energy, and biotechnology received unprecedented push. The global recognition India enjoys today in these fields is a direct result of consistent support and visionary policies,' he said. Citing the example of space tech -- once confined to building rockets – which powers telemedicine, farming, and classrooms, Singh highlighted that 'science is no longer confined to labs'. Space tech is also improving daily life through applications like agricultural weather updates, and online education. The MoS also highlighted transformative initiatives such as the JAM (Jan Dhan-Aadhaar-Mobile) trinity, the Swachh Bharat Mission, and Special Campaign 4.0. Improving social security, PM Modi-led government also launched progressive pension reforms benefiting women. Family pensions will now continue for childless widows even after remarriage, and divorced daughters are entitled to family pensions if divorce proceedings were initiated while their parents were alive, said Singh, adding the upcoming tenure 'will be about accelerating the gains of the last decade'. Meanwhile, the Science and Technology Minister also reaffirmed India's leading role in global climate action, urging citizens and institutions alike to adopt sustainable practices as a national duty. He underlined that India's approach to climate resilience is rooted in both scientific innovation and public participation. 'Earth gives us everything -- clean air, fresh water, fertile land. But we take these gifts for granted,' Singh said, addressing a virtual event organised by the Department of Science and Technology (DST) on the occasion of World Environment Day. Warning the increasing threats from pollution, deforestation, and climate change, he said that combating these challenges must become a collective responsibility, enabled by behavioural change and lifestyle-driven movements like Mission LiFE—Lifestyle for Environment.
Time of India
01-05-2025
- Business
- Time of India
Lend, baby, lend: Borrowers getting younger
MUMBAI: When HDFC began offering home loans in the late 1970s, credit was a privilege, accessed cautiously and late in life. A large downpayment was a prerequisite, and only those in their 40s, with years of savings, could typically afford to borrow. Today, credit is accessed much earlier and more casually, with a growing number of Indians starting their credit journey in their mid-20s. The average age at which Indians avail their first credit product has fallen by 21 years across three generations, a study by Paisabazaar showed. K Cherian Varghese, former chairman of Corporation Bank, who began his banking career five decades ago says that the ability to borrow is directly linked to the size of disposable income. " Personal loans are taken either to finance purchase of some asset like home loan or for consumption. In the 70s, the disposable income was not enough to service the loan EMIs. Today, in many jobs like IT, the employees get a decent salary that allows them to meet all expenses and repay their EMIs," said Varghese. He adds that today banks have pre-approved many corporate employers and are willing to provide a suite of credit products to their employees making credit much more accessible. While the credit bureau made it easier for lenders to identify delinquents, the Jan Dhan-Aadhaar-Mobile trinity made it easier to keep track of borrowers. The study, which is based on the credit behaviour of over 10 million consumers, showed that those born in the 1960s began borrowing at 47, largely through secured loans like mortgages. In contrast, individuals born in the 1990s typically enter the credit ecosystem by the age of 25-28, often through unsecured products such as credit cards, personal loans, and consumer durable loans. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Trend: This unbreakable health-tracking watch is delighting seniors Indestructible Smartwatch Undo This shift reflects the broader trend of easier access to credit and a changing consumer mindset - one that values instant gratification over prolonged savings. While the first credit card in India was launched by Central Bank with Mastercard in the early 80s, it was a very highly restricted product. The card was offered largely to tax-paying high earners. It wasn't until Citi, SBI and ICICI started offering cards to a wider audience that cards picked up. The study traces how the entry point into the credit system has evolved. For those born in the 1960s, home loans were the first credit product. For the 1970s and 1980s cohorts, auto loans became the preferred starting point, availed at average ages of 39 and 31, respectively. The average age for a first-time home loan borrower has declined from 41 (for those born in the 1970s) to 28 (for the 1990s-born). Stay informed with the latest business news, updates on bank holidays and public holidays . AI Masterclass for Students. Upskill Young Ones Today!– Join Now
