By Sunil Pandita - SVP and Head of Business, India And South Asia, Newgen Software
AI in banking
In an era of relentless digital disruption, financial services firms are no longer simply exploring artificial intelligence (AI), they are deploying it at scale to shape the future of banking. From automating credit decisions to redefining customer engagement, AI is redefining how banks operate, compete, and deliver value.
Globally, the trajectory of AI in financial services reflects both its transformative potential and its inevitability. The global AI-in-finance market is projected to expand from US$38 billion in 2024 to nearly US$190 billion by 2030, at a CAGR exceeding 30%. Within banking alone, estimates suggest a rise from US$26 billion in 2025 to over US$330 billion by 2034, underscoring the sector’s conviction in the power of intelligent automation and analytics.
In India, the trend is equally pronounced. The Indian market for AI in banking and financial services is expanding rapidly. According to IMARC Group, the AI in the BFSI market in India was valued at US$830 million in 2024 and is projected to reach US$8.09 billion by 2033, growing at a CAGR of nearly 29%.
These trends reflect a clear reality: intelligence is no longer optional. It’s a structural necessity for India’s banks to compete in a data-driven economy.
From Data to Decisions: The Intelligence Imperative
Banking has always been about foresight: the ability to read risk and opportunity before others do. Artificial intelligence has now industrialised that instinct.
By combining structured and unstructured data, AI transforms fragmented information into foresight. Machine-learning models read borrower intent from financial statements, identify high-risk behaviour early, and flag compliance gaps before they become exposures.
Across India, financial institutions are already deploying AI to detect fraud in real-time, expedite loan approvals, and deliver personalized investment recommendations through chatbots and virtual assistants. Predictive analytics and sentiment analysis now help banks tailor engagement strategies, anticipate churn, and optimise portfolio performance.
Applications of AI in Financial Services
AI is reshaping how India’s financial institutions operate, enabling banks and NBFCs to manage risk, enhance engagement, and make smarter, faster decisions. From intelligent virtual assistants to adaptive, goal-driven systems that learn and evolve, the emergence of Agentic AI is amplifying this shift. Here are a few ways in which AI is transforming the financial service industry.
1. Intelligent Deposits Growth Engine
Banks are increasingly pairing GenAI with data science to understand deposit behaviours at a granular level. Scenario-led analyses highlight under-penetrated segments, optimise portfolio mix, and sharpen cross-sell strategies. With agentic AI continuously monitoring patterns and modelling rate sensitivities, deposits shift from static balances to deliberate growth levers.
2. Automated Document Classification & Data Extraction
KYC and loan documentation remain paper-intensive, and AI now fills the gap with precision and speed. Intelligent models classify documents, extract relevant data, and flag discrepancies in real time. Agentic AI adds contextual understanding, resulting in faster onboarding, fewer errors, and stronger regulatory alignment.
3. Automated Spreading and Credit Analysis
Underwriting is being streamlined by AI that automates financial statement analysis, normalises ratios, and surfaces anomalies. Agentic AI supports credit officers with prompt-based insights throughout the lifecycle, from pre-qualification to disbursal, enabling quicker, more consistent, and evidence-backed lending decisions.
4. Intelligent Cross-Selling and Customer Engagement
AI-driven intent engines analyse behavioural and transactional data to anticipate customer needs. Agentic AI enhances this by autonomously engaging across channels, delivering personalised recommendations in real time. Several Indian private banks are already leveraging these capabilities for SME lending and wealth advisory.
5. Loan Portfolio Monitoring and Early-Warning Insights
AI models now track portfolio health continuously, identifying emerging risk clusters and predicting delinquencies. Agentic AI acts as a digital sentinel, detecting stress indicators early and issuing timely alerts, helping lenders intervene proactively and protect asset quality.
6. Collections and Recovery Intelligence
Collections are evolving from reactive to predictive. AI segments borrowers by repayment probability and prioritises outreach, while agentic AI automates communication cycles, adapts strategies on the fly, and improves recovery outcomes with lower operational effort.
The colossal impact of AI in the financial industry is no longer hypothetical.
As adoption deepens, efficiency gains and cost reductions will accelerate. According to the EY GenAI Study 2025, over two-thirds of Indian financial executives expect GenAI to enhance customer service and risk management within three years. When applied across the full banking lifecycle, from onboarding and underwriting to collections, AI’s compound impact can reshape performance metrics, governance, and customer trust alike.
Navigating the Challenges
However, intelligence at scale is not without its caveats. Data fragmentation, legacy integration, model bias, and regulatory uncertainty remain real impediments. European studies suggest that nearly half of AI initiatives in financial institutions still remain in pilot or limited production phases due to data governance gaps and compliance complexities.
Responsible AI adoption demands a threefold approach:
- Governance: Ensuring transparency, explainability, and traceability of algorithms.
- Data Integrity: Unifying data sources under secure, compliant architectures.
- Talent and Culture: Fostering collaboration between domain experts, data scientists, and compliance officers to sustain AI’s momentum responsibly.
The Road Ahead
As banks transition from being digital enterprises to intelligent ecosystems, the contours of competition are being redrawn. The winners will not necessarily be the largest or the most digitally mature, but rather those that can translate intelligence into foresight, speed, and personalization.
AI’s promise in financial services lies not merely in automation, but in elevation, enabling banks to evolve from transactional entities to trusted advisors.
The message for financial leaders is clear: in the next decade of banking, intelligence will redefine leadership.
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