Generative Artificial Intelligence in Banking – Applications, Risks and Way Forward

Abstract

Generative Artificial Intelligence (GenAI) is emerging as a transformative force in the banking sector, extending the capabilities of traditional artificial intelligence from prediction and classification to the generation of novel content, reasoning narratives, code and synthetic data. For banks that have historically deployed AI primarily for credit scoring, fraud detection and risk analytics, GenAI enables deeper automation, enhanced human decision support and more contextual customer engagement across the value chain. Recent estimates suggest that GenAI could deliver substantial productivity gains and new revenue opportunities globally, with particularly strong potential in the Indian banking system given its scale, digital maturity and data richness.

At the same time, the adoption of GenAI introduces a distinct and complex risk landscape, encompassing model hallucinations, bias and discrimination, explainability challenges, data protection concerns, conduct risks, operational dependencies and potential systemic effects. This paper analyses the key applications of GenAI across customer experience, credit and risk management, compliance, operations and enterprise functions, with specific illustrations from Indian banks and financial institutions. It further examines the associated risks from a prudential, consumer protection and governance perspective, situating the discussion within the Reserve Bank of India’s Framework for Responsible and Ethical Enablement of AI in Finance (FREE-AI). The paper concludes by outlining a pragmatic way forward for banks, emphasising human oversight, robust governance, ethical design and regulatory collaboration to ensure that GenAI adoption enhances efficiency and inclusion while preserving safety, soundness and trust in the financial system.

1.   Introduction

Generative Artificial Intelligence (GenAI) represents a class of AI models capable of producing new content – text, code, images, audio, and synthetic data – based on patterns learned from large datasets. For banks that have been leveraging AI/ML capabilities majorly in areas like credit scoring and fraud detection, GenAI allows them to refine their rule-based decision making further by creation of complex reasoning models, generation of novel text, images and codes.

It is estimated that GenAI could potentially add US$200–340 billion annually to the banking sector globally through productivity gains and new revenue streams (McKinsey & Company, 2023). The outlook for India is also sanguine; recent studies indicate that that GenAI could improve banking operations productivity by up to ~46% by 2030, especially in areas such as operations, customer service, technology, risk and compliance (EY, 2025) (India Brand Equity Foundation, 2025).

However, introduction of any new technology also comes with associated risks which in case of GenAI majorly includes hallucinations, perpetuation of biases and model drift.

Hence at attempt is made in this article to analyse the applications of GenAI in banking while also examining the associated risk landscape, with particular reference to the Indian context.

2.   Generative AI: Overview and Relevance to Banking

2.1  What is Generative AI?

 Generative AI is a type of artificial intelligence that deploys special machine learning models (eg-Large language models LLMs) that learn the structure of the data and then generate outcomes. For example, a generative model trained on text can draft

emails or reports, while one trained on images can create new pictures from short descriptions.

2.2  How it is different from Traditional AI systems?

Generative AI differs from traditional AI primarily in its ability to create new content rather than just analyze or classify existing data. Traditional AI excels at predictive tasks using rule-based or supervised learning, while generative AI produces original outputs like text or images by learning patterns from vast datasets.

Table-1: Comparison of Traditional AI to Generative AI

Aspect	Traditional AI	Generative AI
Core Function	Analyzes data for predictions/classification	Generates novel content (text, images)
Approach	Rule-based, deterministic algorithms	Probabilistic, deep learning (GANs, transformers)
Data Needs	Smaller, structured datasets	Massive, diverse datasets
Output Type	Insights, decisions	Creative, original media
Real-Time Suitability	High (low latency)	Lower (resource-intensive)

Source: (McKinsey & Company, 2023)

2.3   Key capabilities:

The key capabilities of GenAI that can be leveraged by the banking sector include:

1. Natural language understanding and generation: which relates to answering queries, summarising documents, drafting emails and reports, extracting entities from unstructured text.

2. Code generation and refinements: assisting developers and risk modelers with code suggestions, test cases and documentation.

3. Analysing semi-structured and unstructured data: GenAI can combine tabular, text and other inputs to support decisions

4. Synthetic data generation: creating privacy-preserving synthetic datasets for model development and testing.

2.4  Drivers of GenAI Adoption in Banking

Key forces driving GenAI adoption in banking include:

a. Cost and productivity pressures: with increasing economic pressure and competition, banks need to reduce cost-to-income ratios and improve operational

b. Digital customer expectations: With the increasing digitalisation of banking services, the demand for 24×7, personalised and contextual engagement is increasing by the

c. Data deluge: With the growing volumes of unstructured data (emails, PDFs, call transcripts, documents), a need is being felt on analysing such information.

d) Competitive dynamics: fintech-led innovation and big tech capabilities pushing banks to modernise.

As per estimates, it is believed that GenAI-enabled process redesign could automate or augment 30–50 % of tasks in select banking functions, with the greatest impact being felt in operations, technology, risk/compliance and customer service (EY, 2025) (KPMG, 2025).

3.   Applications of Generative AI in Banking

 GenAI has applications across the entire spectrum of banking operations. Examples of area specific applications are provided below:

3.1  Customer Experience and Front-Office

3.1.1 Intelligent Virtual Assistants and Chatbots

Banks are deploying GenAI-powered conversational agents across mobile apps, WhatsApp, IVR and web channels to answer FAQs on accounts, loans, cards, UPI, complaint status and documentation requirements, guide customers through journeys such as account opening, loan applications or dispute resolution, provide multilingual support, including Indian languages, using speech-to-text and text-to-speech integrations and escalate complex queries to human agents with summarised context.

Compared to rule-based bots, GenAI facilitates engagement through more natural dialogues, better intent recognition and dynamic responses, thereby improving containment rates and customer satisfaction (AISERA, 2025). Indian private banks, payments banks and NBFCs are piloting such GenAI bots for retail customers and MSMEs.

3.1.2    Personalised Sales and Financial Advice (Under Human Oversight)

GenAI can analyse unstructured data like customer transaction histories and merge it with structured data like demographics and product holdings so as to suggest personalised product offers (e.g. credit cards, term deposits, insurance riders. For example, State Bank of India (SBI) is integrating hyper-personalization into its YONO

2.0 platform to offer nudges for savings based on spending behavior. It also helps in simplifying the product features, fees and risks for the customer to understand while also supporting relationship managers with “next best action” suggestions and tailored pitches.

In India, GenAI assistants are being trained to help relationship managers prepare customised proposals for SME and corporate clients, by extracting information from internal data, sectoral insights and regulatory updates (Infosys Finacle, 2024). However, such tools must operate under robust suitability and mis‑selling safeguards, with the decision making responsibility still retained with humans.

3.1.3    Multimodal Interfaces and Accessibility

By combining voice, text and image understanding, GenAI can make banking more accessible to differently abled customers and low‑literacy segments. Further, GenAI through multilingual conversational banking, is attempting to bridge the language divide in a linguistically diverse country. Some of the applications include introduction of voice-based interactions for visually impaired customers or those uncomfortable with typing, translation and summarisation of complex documents (e.g. loan agreements, KFS) into simple language, possibly in local languages and extending assistance in filling forms by extracting relevant fields from documents and auto-suggesting entries.

These capabilities align with financial inclusion objectives, provided they respect privacy, obtain consent and are designed for clarity and fairness.

3.2  Credit, Risk and Lending

3.2.1 Credit Underwriting and Review

GenAI can strengthen the loan underwriting mechanism in banks by extracting key financials from bank statements, GST returns, ITRs and financial statements, summarising qualitative information from management discussions, industry reports and news and assisting analysts in drafting credit notes, risk assessments and rating rationales.

Some Indian lenders report faster turn-around times for SME and unsecured loan underwriting when GenAI is used to pre‑process documents and generate draft credit memos, which are then reviewed and finalised by credit officers (Infosys Finacle, 2024) (Human in the loop). This can be particularly impactful in MSME and supply-chain finance where documentation is heterogeneous and unstructured.

3.2.2    Early Warning Systems and Portfolio Monitoring

GenAI-powered tools can be used to continuously scan news, social media, rating changes and sectoral reports for adverse signals around borrowers or sectors, generate early warning summaries for relationship managers and risk teams and support stress-testing narratives by linking macroeconomic scenarios with portfolio impacts.

These capabilities complement traditional quantitative EWS metrics and can enhance forward‑looking risk management.

3.3  Fraud Management, AML and Compliance

3.3.1 Fraud Detection and Investigation Support

GenAI can enhance fraud management by generating risk narratives from complex transaction graphs to assist fraud investigators, automate first‑level triage of alerts by summarising patterns, related parties and historical behaviour and providing chat-based interfaces for investigators to query case histories and knowledge bases.

While core detection algorithms may continue to rely on specialised ML models and rules, GenAI adds an interpretative and investigative layer that can reduce the analyst workload (Fineksus, 2025).

3.3.2  AML, KYC and Regulatory Compliance

In AML and compliance, GenAI can help parse and interpret new regulatory circulars and guidelines, highlighting changes and obligations, assist in drafting Suspicious Transaction Reports (STRs) and other regulatory submissions based on structured alert data and generate summaries of customer risk profiles and due diligence findings. It can also serve as a knowledge assistant for compliance staff, answering queries based on internal policy libraries and regulations. Such tools can reduce manual effort and improve consistency, but they must be deployed within strict governance, audit and data-protection boundaries.

3.4  Operations, Technology and Enterprise Functions

3.4.1 Document Processing and Knowledge Management

Banks process large volumes of documents – loan applications, collateral documents, contracts, board papers, policy manuals and technical documentation. GenAI can assist in automated classification, tagging and extraction of key clauses or fields, summarisation of lengthy documents for quick review by management or committees and facilitate conversational search over internal knowledge repositories (“ask your bank’s knowledge base”).

Indian institutions increasingly use GenAI-based internal copilots for employees, improving productivity in operations, HR and legal functions (EY, 2024).

3.4.2  Software Development and IT Operations

GenAI-based coding assistants and DevOps copilots can suggest code snippets, unit tests and refactoring options, generate documentation and API specifications and help analyse logs, configuration files and error messages to accelerate incident resolution.

Given the complexity of legacy core systems and regulatory constraints, such tools can significantly enhance developer productivity and system reliability, provided they are used within secure, controlled environments.

3.4.3  Finance, HR and Support Functions

 In central functions, GenAI can assist in drafting of internal notes, board memos, minutes and management reports, designing of scenario narratives for financial planning and budgeting and automating HR tasks such as job-description drafting, candidate screening summaries and learning content creation.

These applications are largely inward-facing and can serve as low‑risk pilots to build GenAI capabilities and organisational comfort.

3.5 India Specific Applications-Examples

 The following use cases were observed across select entities in the Indian financial system.

Table-2: GenAI applications in select financial institutions in India

Bank / Entity	Primary Function Area	Reported GenAI Application
HDFC Bank	Customer interface / service	GenAI-augmented chat/voice bots for multilingual customer queries, product explanation, and complaint handling across digital channels.
ICICI Bank	Customer interface / marketing	LLM-based content generation for customer emails/SMS, FAQs, and personalized campaign text at scale.
Axis Bank	Internal productivity / knowledge management	Internal GenAI copilots to draft internal notes, summarize policy and product documents, and assist RM frontline teams.
State Bank of India (SBI)	Customer interface / financial literacy	Next-generation versions of “YONO” and chatbot services enhanced with GenAI for conversational support and vernacular financial education content.
Kotak Mahindra Bank	Risk & credit / analytics support	Use of LLMs to create narrative credit memos, scenario descriptions, and portfolio commentary from quantitative data.
YES Bank / mid-tier private banks	Compliance / regulatory reporting	GenAI to parse circulars and regulations, generate first drafts of compliance checklists and internal policy updates.
Large NBFCs & payment banks	Fraud & transaction monitoring (pilot stage)	Using GenAI with traditional models to generate suspicious-pattern scenarios, analyst notes, and alert explanations for investigation teams.
Indian banks on global clouds (e.g., via AWS / Google Cloud / IBM)	Cross-cutting (customer, ops, risk)	Sandboxed GenAI pilots using cloud providers’ banking GenAI stacks for chatbots, document processing, and code assistance while keeping data residency controls.

Source: Information collated by the author from websites of financial institutions.

4.   Risk Landscape of Generative AI in Banking

 While GenAI offers substantial opportunities, its deployment in banking raises a multi-dimensional risk landscape. Risks can be grouped into model/technical, data/privacy, conduct and consumer, operational and systemic perspectives.

4.1  Model and Technical Risks

 4.1.1 Hallucinations and Reliability

GenAI models can produce plausible but incorrect or fabricated outputs (“hallucinations”), especially when asked for specific facts, calculations or out-of-scope queries (Phillips, 2024). In banking, this can manifest as incorrect regulatory interpretations, product features or fees that is misspecified, inaccurate summaries of financial documents, etc. These hallucinations if not mitigated can lead to customer detriment, mis‑selling, operational errors and reputational damage.

4.1.2  Bias, Fairness and Discrimination

Biases in training data or design can result in discriminatory outputs – for example, differential treatment across gender, caste, region or socio‑economic status in customer profiling or underwriting narrative (Bain & Company, 2025). While final credit decisions may be based on separate scorecards, biased GenAI-generated insights or language can influence human decision‑makers and propagate unfair practices.

4.1.3  Explainability and Black-Box Concerns

LLMs and other GenAI architectures are complex and opaque. Their internal reasoning is not easily interpretable, which complicates processes like model validation and back‑testing and regulatory scrutiny and audit.

Regulators, including the RBI under FREE‑AI, emphasise the need for explainable AI, documentation and traceability of AI-driven processes (Reserve Bank of India, 2025) (Lakshmikumaran & Sridharan, 2025).

4.2  Data Protection, Privacy and Cybersecurity

4.2.1Data Leakage and Unauthorised Usage

If sensitive customer or transaction data is fed into external GenAI services or inadequately protected internal systems, associated risks could emerge like data retention by third-party providers contrary to the bank’s policies or being used to further train models in ways incompatible with privacy requirements. This raises compliance issues under data protection norms and banking secrecy requirements.

4.2.2  Prompt Injection and Adversarial Attacks

GenAI systems are susceptible to prompt injection and adversarial inputs, where malicious content embedded in documents or user prompts attempts to override system instructions, exfiltrate data or execute unintended actions (Thomson Reuters, 2024).

In an agentic setting where GenAI interfaces with bank systems (e.g. to perform limited actions), such attacks could escalate into operational and security breaches if not properly sandboxed and constrained.

4.3  Conduct and Consumer Protection Risks

4.3.1 Mis‑selling and Unsuitable Recommendations

If GenAI tools directly or indirectly provide product recommendations to customers without adequate guardrails, there could be problems like omission of key risk disclosures. optimistic presentation of products, failure to account for suitability, affordability and KYC norms, etc. Even if disclaimers exist, customers may reasonably assume that information from a bank-branded AI assistant is authoritative, which heightens conduct risk.

4.3.2  Over-Reliance and Automation Bias

 Employees and customers may over‑trust GenAI outputs (“automation bias”), using them without adequate verification, especially when responses are fluent and confident. This can weaken judgement, increase error propagation and complicate assignment of responsibility.

4.4  Operational, Third-Party and Concentration Risks

4.4.1 Vendor and Model Concentration

Banks may rely on a small number of foundation model providers or cloud platforms, resulting in concentration of operational risk, dependency on proprietary models and closed ecosystems and cross‑institution escalation of correlated failures in case of outages or model defects.

This raises questions on resilience, exit strategies and portability of GenAI deployments (Leitner et al., 2024).

4.4.2  Governance and Skill Gaps

Effective GenAI governance requires interdisciplinary expertise across risk, technology, legal, compliance and business. Many institutions face skill gaps, fragmented ownership and immature internal processes. Weak governance can amplify other risks, including inadvertent policy breaches or shadow IT deployments.

4.5  Systemic and Macro-Financial Risks

At a system-wide level, GenAI raises concerns around ‘Herding behaviour’ i.e. if many institutions rely on similar GenAI-powered models or insights, their actions may become more correlated, potentially amplifying procyclicality and market swings. In addition, malicious actors can use GenAI to craft more convincing phishing, deep fakes and social engineering targeted at customers and staff resulting in amplification of threat. Further, accelerated automation may impact employment profiles in operations and call centres, necessitating reskilling and social considerations.

Central banks and international standard setters are beginning to explore these macro‑prudential dimensions, though this remains an evolving area (TCS, 2025).

5.  Regulatory and Supervisory Context: The RBI FREE‑AI Framework

Recognising both opportunities and risks, the RBI’s “Framework for Responsible and Ethical Enablement of AI in Finance” (FREE‑AI) provides a principle‑based and risk‑proportionate roadmap for AI adoption in Indian finance (Reserve Bank of India, 2025) (Lakshmikumaran & Sridharan, 2025). The framework lists seven sutras which can serve as the frame of reference for the banks to build design their risk management framework.

5.1  Scope and Principles

FREE‑AI applies to RBI‑regulated entities (banks, NBFCs, payment system operators, etc.), their group entities and material service providers. It adopts seven ethical “Sutras” including:

Trust and Accountability

• People‑First and Human Oversight
• Fairness, Non‑Discrimination and Inclusion
• Transparency and Explainability
• Robustness and Safety
• Data Protection and Security
• Sustainability and Resilience

These principles are intended to cover all AI systems, including GenAI and agentic AI.

6.   Conclusion

Generative AI has the potential to transform banking by enhancing productivity, customer experience and risk management across the value chain. For Indian banks, the combination of strong digital public infrastructure, a vibrant fintech ecosystem and a principle-based regulatory framework such as FREE‑AI creates fertile ground for responsible innovation.

However, GenAI also introduces a new layer of model, data, conduct, operational and systemic risks. Realising its benefits will therefore require banks to move beyond isolated pilots towards strategic, well‑governed adoption, anchored in robust controls, human oversight and ethical principles.

Over the next three to five years, successful institutions are likely to be those that (i) identify high‑impact, low‑regret GenAI use cases; (ii) invest in governance and technical foundations; (iii) cultivate AI‑literate, accountable teams; and (iv) collaborate with regulators and industry peers to shape trusted AI ecosystems. In doing so, the banking system can harness GenAI as a force multiplier for inclusion, resilience and innovation, while upholding safety, soundness and public trust.

Authored by:

 

 

 

Reema Tripathy

Assistant General Manager

Reserve Bank of India