AI IN BANKING SECTOR: SIGNIFICANCE, CHALLENGES, WAY FORWARD

Copyright infringement not intended

Picture Courtesy:  AINEWS

Context

Malaysian conglomerate YTL Group and Singapore's Sea Ltd have launched Ryt Bank, the world's first AI-powered bank, a major leap for artificial intelligence in the Banking sector.

Artificial Intelligence (AI) in Banking Sector 

AI refers to machines performing tasks that normally require human intelligence, such as learning, reasoning or language understanding.

In banking, AI uses advanced algorithms and data analysis to automate decisions, predict trends, and personalize services.

Unlike ordinary software, AI “learns” over time from data, improving its accuracy and expanding its abilities.

Significance of AI in the Indian Banking Sector

Enhanced Fraud Detection and Cybersecurity: AI algorithms analyze transaction patterns in real-time to flag anomalies that humans would miss.

• Axis Bank's AI-driven credit risk analysis is so effective that 80% of suspicious transactions originate from the 5% of customers the AI identifies as high-risk.

Personalized Customer Experiences: By analyzing transaction history and spending patterns, AI delivers customised product recommendations and financial advice.

• HDFC Bank's chatbot, EVA, uses Natural Language Processing (NLP) to answer millions of customer queries in milliseconds, providing personalized, conversational banking at scale.

Efficient Credit Risk Assessment: AI processes vast datasets, including alternative data like mobile usage, to enable faster and more accurate loan approvals.  

• ICICI Bank uses AI-driven credit scoring models that analyze non-traditional data, extending credit to individuals without a formal credit history and boosting financial inclusion.

Streamlined Regulatory Compliance and AML: AI automates and enhances regulatory processes like Know Your Customer (KYC) and Anti-Money Laundering (AML), reduce manual effort, helps banks combat financial crime and manage the global AML compliance costs.

• HSBC leverages AI and advanced analytics to detect criminal networks and reduce false positives in its AML transaction monitoring.

Automation of Back-Office Operations: AI-powered Robotic Process Automation (RPA) automates repetitive tasks like data entry and document verification, cutting operational costs and human error.

• ICICI Bank has deployed software robotics in over 200 business processes, cutting customer response times by up to 60% and achieving 100% accuracy.

24/7 Customer Support via Chatbots: AI-powered virtual assistants provide instant, 24/7 responses to customer queries, boost satisfaction while reducing the load on human agents.

• State Bank of India's chatbot, SIA (SBI Intelligent Assistant), handles millions of customer queries daily, offering immediate resolutions for a wide range of banking tasks.

Strategic Decision-Making: AI processes datasets to uncover deep insights into market trends and customer behavior, enabling superior strategic decisions. SBI's analytics team, for example, developed AI/ML models that boosted business and cut risk.

Driving Financial Inclusion: AI expands banking to underserved populations by using alternative data for credit scoring and offering voice-based services in regional languages.  

• IndusInd Bank's voice assistant 'IndusAssist' uses Amazon's Alexa to let customers conduct transactions via voice commands, improving accessibility for those not proficient with apps or websites.

Initiatives and Regulation for AI in Indian Banking Sector

RBI Committees and Guidelines: In Dec 2024 the RBI formed a panel to develop a “Framework for Responsible and Ethical AI (FREE-AI)” in finance.

• In August 2025 RBI released 7 guiding “sutures” (principles) such as Trust, People First, Fairness, Accountability, Explainability, and Safety. These principles stress that AI systems must be reliable, non-discriminatory, human-centric and secure.

Risk Detection Tools: RBI’s Innovation Hub launched MuleHunter AI, a system that quickly spots mule (fraud) accounts across banks.

• RBI’s recent digital lending rules mandate that any AI-based loan decision be explainable, auditable, and come with human oversight and grievance redressal.

Policy Frameworks: The RBI’s FREE-AI framework calls for building shared financial data infrastructure (an “AI-Kosh”) and an “AI Innovation Sandbox” where banks can safely test AI models on anonymized data.

• It also recommends regular AI risk audits (red teaming) and an incident-reporting mechanism to promptly catch and disclose AI failures.
• Capacity-building is emphasized: banks should train staff in AI governance and share best practices sector-wide.

Securities and Finance Boards: SEBI issued a 2025 consultation on responsible AI use in securities markets. The Government’s IndiaAI Mission and other programs promote AI R&D and startups in finance.

Digital Infrastructure: India’s strong digital public infrastructure (Aadhaar ID, UPI payments, account aggregators) provides a base for AI.

• RBI notes AI can enhance these systems to offer personalized and inclusive services. For example, e-KYC and AePS (Aadhaar-enabled payments) use biometrics and AI for secure access.

Global Alignment: India is following global trends: like many countries, regulators stress explainability and fairness.

• RBI Governor has warned against over-reliance on just a few AI vendors due to systemic concentration risk. The goal is balanced oversight – promoting innovation while protecting customers and stability.

Future of AI in Banking (Emerging Trends)

Generative AI: Large language models (LLMs) and generative AI will enable new services (automated report summaries, contract drafting, intelligent chat). RBI projects that generative AI could directly improve banking efficiency by up to 46%.

• The RBI report said that generative AI alone is projected to exceed Rs 1.02 lakh crore by 2033, growing at an annual rate of 28-34%.

Data Analytics and Predictive Services: AI for real-time risk forecasting, dynamic pricing and personalized wealth management.

• Predictive analytics (AI models analyzing big data) will help in preempting loan defaults or identifying cross-sell opportunities.

Integrated IoT Finance: AI will integrate with emerging tech – for example, wearable devices or IoT networks could feed data to banks for contextual financial offers.

• Insurance premiums and loans might be dynamically priced based on sensor data (like a connected car’s driving data).
• Some institutions are exploring combining AI with blockchain, for automating financial agreements that adjust terms in real time.

Quantum and Advanced Computing: AI combined with quantum computing could solve complex risk models much faster, improving security and optimization in banking.

RegTech and Explainable AI: As AI use grows, so will tools for regulation. Banks will adopt explainable-AI methods (XAI) to make models transparent, in line with regulators’ expectations.

• Automated compliance checks (RegTech) will use AI to scan transactions and reports.

Digital Allies: More banks will partner with fintech and BigTech.

• For example, generative AI assistants may be co-developed with cloud/tech giants.
• Appointing AI “risk officers” – machine learning systems assessing bank-wide risk exposures.

Challenges and Concerns to Widespread Adoption

Algorithmic Bias and Fairness: Algorithmic bias is not just as a technical flaw but as a modern form of "digital redlining." If left unchecked, it can perpetuate and even deepen historical inequalities, creating new barriers to economic mobility and contradicting the constitutional promise of equality.

Data Privacy and Security in a Third-Party Ecosystem: AI's use of vast amounts of sensitive customer data creates a massive attack risk, which magnified as banks depend on third-party vendors.

• In April 2025, a ransomware attack on vendor Toppan Next Tech compromised the personal data of over 11,000 customers from DBS and Bank of China.

The "Black Box" Problem and Lack of Interpretability: Complex deep-learning models operate as a "black box," making it impossible for humans to understand or explain their decisions.

• It erodes customer trust and creates a compliance challenge when regulators demand justification for actions like a approval/denied loan.

The AI Cybersecurity Arms Race: While banks use AI for defense, threat actors are weaponizing it to launch hyper-realistic and sophisticated attacks. This includes AI-powered phishing, advanced ransomware, and convincing deepfake scams that can bypass traditional security.

• In July 2025, a senior executive at Deutsche Bank India was defrauded of over ₹1.08 crore after being tricked by a deepfake video call that perfectly mimicked the company’s CEO.
• According to the RBI, ₹3,207 crore was lost because of 5,82,000 cases of cyber fraud between FY2020 and FY2024.

Job Displacement and the Urgent Need for Reskilling: While AI can boost productivity, it risks deepening the problem of jobless growth.

• Challenge for policymakers is not just how to reskill, but how to create new, high-value jobs that can absorb a workforce whose traditional roles are becoming obsolete.  

Vendor and Concentration Risk: Over-reliance of the entire banking sector on a handful of BigTech firms for cloud computing and AI services (like Google Cloud, and Microsoft Azure) creates a systemic risk.

• A single security flaw in one of these providers could cripple multiple financial institutions simultaneously.

Talent Shortage and Skill Gap: A 2025 Quess Corp report identified a 42% AI and data talent skill gap within India's Banking, Financial Services, and Insurance (BFSI) sector.

Regulatory Uncertainty and Evolving Compliance: Pace of AI innovation has left regulators worldwide in a race to catch up, creating a complex and uncertain compliance landscape.

• Banks must navigate a patchwork of evolving rules that differ across jurisdictions.
• European Union's AI Act (2024) categorized financial AI as "high-risk" and imposed strict transparency requirements, has set a global benchmark that international banks, including those in India, can adopt as model.

Way Forward of AI in Indian Banking

Establish a National Framework for Ethical AI in Finance: Include mandatory AI ethics committees within banks and a "human-in-the-loop" requirement for all critical decisions, such as high-value loan rejections.

• Operationalize the RBI's proposed FREE-AI, making its seven principles auditable standards for all financial institutions.  

Develop Sovereign AI Capabilities and Reduce Vendor Risk: Avoid over-reliance on a few global tech giants, encourage development of indigenous AI ecosystem, aligns with the goals of data sovereignty and Atmanirbhar Bharat.

• Leverage the IndiaAI Mission, with its ₹10,372 crore outlay, to fund R&D for Indian Large Language Models (LLMs) and support domestic fintechs in creating AI solutions tailored for the Indian financial context.

Strengthen Data Infrastructure with a Focus on Privacy: Build shared data platforms that allow banks to train AI models on high-quality, anonymized data.

• "AI-Kosh" can provide the necessary data fuel for AI models while complying with the strict consent requirements of Digital Personal Data Protection (DPDP) Act, 2023.  

Mandate Explainable AI (XAI) for Transparency: Address the "black box" problem by mandating that all AI models used for credit scoring and other customer-facing decisions be interpretable.

• Follow the example of companies like Mastercard, which uses Explainable AI (XAI) to reduce false positives in fraud detection.

Innovation Through Regulatory Sandboxes: Encourage experiment in a controlled environment to allow banks and fintechs to test new AI models without posing a risk to the live financial system.

• RBI's Regulatory Sandbox (RS) program should be expanded to include testing advanced GenAI applications.

Launch a Sector-Wide Skilling Mission: Address the talent deficit by launching a upskilling and reskilling initiative focused on AI ethics, data science, and AI-driven risk management.

• Partnerships between banks, ed-tech firms, and top academic institutions like the IITs, and expanding reach through Skill India Mission.

Enhance AI-Specific Cybersecurity Safeguards: Develop a dynamic cybersecurity strategy that uses AI to defend against AI-powered threats, defenses against deepfakes, data poisoning, and other emerging attack vectors.

Promote a "Phygital" Model for Financial Inclusion: Ensure that AI-driven digital transformation bridge digital divide, blend digital efficiency with human-led last-mile connectivity.

• Empower Business Correspondents (BCs) with AI-powered handheld devices, to offer real-time credit scoring and personalized product information in regional languages, bringing the power of AI to rural and semi-urban customers.

Provide Final Regulatory Clarity: Government and RBI must finalize the legal "rules of the road" for AI in finance to provide certainty for long-term investment.

• Finalize the AI-specific regulations under the DPDP Act, 2023, and provide clear guidance based on the "Innovation over Restraint" principle.

Conclusion

Artificial intelligence is transforming banking sector by automating processes, improving risk management and personalizing services. In India, AI can deepen inclusion and make banks more efficient. However, realizing AI’s full potential requires addressing challenges around bias, security, privacy and cost. 

Source: AINEWS