As financial institutions seek to enhance operational efficiency, customer engagement, and risk management, Artificial Intelligence (AI) and Generative AI (GenAI) are emerging as transformative technologies. From fraud detection to personalized wealth management, banks and financial services firms are increasingly integrating AI-powered tools to stay competitive. This article explores the current applications, industry impact, and future outlook for AI and GenAI across the financial services landscape.
Background and Context
Over the last five years, AI adoption in financial services has accelerated, fueled by a combination of regulatory shifts, fintech competition, and digital-first consumer behavior. According to a 2025 report by the Financial Technology Association of India, 67% of Indian banks are now leveraging AI in some form, with a significant focus on risk analytics and customer service automation.
The emergence of Generative AI—capable of producing text, images, code, and even financial reports—has opened new dimensions for automation and decision-making. With large language models (LLMs) like ChatGPT and Claude being integrated into back-end operations and client-facing platforms, financial firms are reimagining how they interact with data and customers.
Key Applications of AI and GenAI
Fraud Detection
AI models trained on historical transaction data can now detect anomalies and suspicious behavior in real time. These models use behavioral biometrics, location patterns, and spending history to flag potentially fraudulent activities, significantly reducing false positives and manual reviews.
“AI-driven fraud systems have improved detection accuracy by over 40% compared to traditional systems,” notes a 2024 Reserve Bank of India (RBI) cyber risk paper.
Credit Scoring and Risk Assessment
AI enables more nuanced credit evaluations by incorporating alternative data—such as utility payments, mobile transactions, and social signals—into credit scoring models. This has made credit underwriting more inclusive, particularly in rural and semi-urban markets.
GenAI tools are also being used by lenders to simulate economic scenarios and borrower behaviors to assess creditworthiness dynamically.
Customer Service and Chatbots
Financial institutions are deploying AI-powered chatbots and virtual assistants to handle millions of routine queries. GenAI adds the ability to interpret complex queries, offer personalized responses, and escalate issues efficiently.
For example, HDFC Bank’s AI assistant Eva now handles over 60% of first-level queries across its digital channels, leading to a 25% reduction in call center costs.
Personalized Financial Advice
Wealth managers are using GenAI to generate customized investment portfolios and financial plans based on a customer’s goals, risk appetite, and financial history. These tools are also capable of generating personalized financial content—from newsletters to retirement summaries—at scale.
“Generative AI allows us to bring mass personalization to financial planning—something that was impossible with manual tools,” said a senior executive at Kotak Wealth Management.
Industry Implications and Competitive Shifts
The use of AI is already changing industry dynamics. Traditional banks that invest early in AI are gaining an edge over fintechs through scale, trust, and deep data pools. Meanwhile, fintech startups are pushing the envelope with more experimental GenAI tools for customer acquisition and product design.
However, the rapid adoption also raises questions about bias in algorithms, data privacy, and explainability—prompting increased regulatory scrutiny. The RBI is reportedly working on a framework for the ethical and responsible use of AI in banking, expected by early 2026.
Stock analysts suggest that firms investing in GenAI show higher operational leverage and NIM stability, particularly those in retail banking, credit cards, and insurance sectors.
Expert Perspectives
Industry leaders are actively shaping the AI narrative.
Rajnish Kumar, former SBI Chairman, remarked at the 2025 India FinTech Forum:
“AI and GenAI are not just cost-saving tools—they’re strategic levers for financial inclusion, credit expansion, and real-time compliance.”
Megha Mehta, CTO at a mid-sized NBFC, highlighted operational shifts:
“We’ve cut our loan disbursement time from 48 hours to under 4 hours using a GenAI underwriting assistant. This wouldn’t be possible with legacy systems.”
Vinay Bansal, Partner at McKinsey India, warned:
“Firms should avoid over-relying on black-box AI models. Transparency and human oversight must remain integral to any AI strategy.”
Social Media Reactions from Industry Observers
@FintechPulseIN:
“AI in finance is no longer optional. Firms not exploring GenAI risk falling behind. #AIinBanking #FutureOfFinance”
@BankTechToday:
“RBI’s upcoming AI guidelines could redefine compliance in the AI age. Watch this space. #RegTech #IndianBanking”
@EconEyes2025:
“Mass personalization in finance is here, thanks to GenAI. The next battleground: trust and explainability. #AIethics #BFSI”
Challenges and Future Outlook
Despite the potential, financial institutions face significant challenges:
Data Privacy and Compliance: AI models need access to vast datasets, often containing sensitive personal information. Ensuring data protection while training models remains a regulatory and ethical concern.
Bias and Fairness: Algorithmic bias, particularly in credit decisions, could perpetuate financial exclusion if not addressed with diverse training data and oversight.
Talent Gap: The demand for AI/ML engineers with BFSI domain knowledge far exceeds supply, pushing up costs and slowing deployment.
Looking forward, AI and GenAI will be central to “Banking-as-a-Platform” models, enabling modular, cloud-native financial products delivered via APIs. Experts predict that by 2030, up to 80% of customer-facing functions in BFSI could be partially or fully AI-augmented.
Conclusion
AI and Generative AI are no longer experimental technologies in financial services—they’re foundational. From streamlining operations to enhancing customer experience and risk management, their role will only grow. As Indian banks and NBFCs invest in these tools, the key will be to balance innovation with governance, ensuring that AI serves both profitability and public trust.
