Hype around AI must not eclipse risk management: RBI

M Rajeshwar Rao, deputy governor of the Reserve Bank of India (RBI), has emphasized that the use of artificial intelligence (AI) in the banking sector should be approached with caution and responsibility. He also warned that the enthusiasm surrounding AI’s benefits and potential must not come at the cost of risk management practices.

“AI in banking sector must be done in a responsible and measured manner. The excitement around AI’s benefits should not overshadow prudent risk management,” Rao said in his speech during a recent event.

“A robust governance is indispensable for ensuring the integrity of data, the reliability of models, and mitigating the risks associated with adoption of AI. The financial institutions should have in place a comprehensive strategy for AI adoption. It should be accompanied by clear policies, risk appetites, criticality, and impact assessments as well as ethical standards that cascade through the organisation,” Rao said at the third edition of the CNBC-TV18 Banking Transformation Summit on ‘Banking That Builds Bharat: AI-Powered, Credit-Driven.’

“Further, in a regulated industry like banking, it is essential to understand how a model arrives at its decisions, making explainability a critical requirement. Thus, there is a need for financial institutions to invest in Explainable AI frameworks that provide clear, auditable reasons for loan decisions. Strong governance is central to managing AI-driven model risk,” he added.

Humans are to be responsible for decisions, while AI can recommend

The deputy governor also emphasized that human beings should be responsible for making decisions, while AI can automate and recommend. India’s financial institutions, while adopting AI for business processes, should leverage the technology as a tool to support and improve human decisions, rather than replace them, he said.

Rao’s emphasis on a human-centric approach for AI adoption may save many jobs, but it also poses a critical challenge because India currently faces a shortage of skilled professionals that can manage and operate AI systems.

Recently, India’s Finance Minister, Nirmala Sitharaman, stated that the country is facing a growing imbalance between the rapid adoption of AI and a shortage of skilled professionals ready to support this transformation. Although AI is emerging as a major catalyst for boosting output and infrastructure planning, a lack of employee skill readiness may threaten the nation’s ability to fully realize its benefits.

At the same time, India’s NITI Aayog, the central public policy think tank of the government, stated in a recent report that labor market transformation will be at the center of India’s AI adoption. As AI technologies mature, they are expected to disrupt global employment, with estimates suggesting that as much as 35–40% of current jobs worldwide are susceptible to some degree of AI-powered automation. This would influence not only advanced economies but also emerging markets like India.

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Privacy-first data handling practices

Rao suggested in his speech that India’s financial institutions adopt robust data strategies, incorporating diverse, reliable indicators that reflect both the scale of AI adoption and related risks. He urged financial institutions to ensure compliance through consent-based, privacy-first data handling practices. This is because when AI is used for credit decisioning or financial inclusion, primarily through the use of alternative data, customer data becomes central, placing a high importance on both privacy and security.

“High-quality data is the backbone of safe and effective AI in finance. While the RBI already collects data through supervisory reports, regulatory returns, and surveys, the introduction of model risk guidelines, aligned with global best practices, will soon extend this scope to include data on AI models used by regulated entities,” Rao pointed out.

“Research efforts of the financial institutions should focus on improving data quality and accessibility, developing novel AI algorithms for enhancing credit inclusion, and addressing key challenges related to bias, fairness, and interpretability in credit evaluation as well as enhancement of in-house capabilities to manage concentration risk of providers,” Rao added.

In December 2024, the RBI established a specialized committee to develop a framework for the ethical use of AI in the financial sector. The RBI brought together experts from various sectors to build a strong, adaptable, and forward-thinking framework that ensures ethical integrity while supporting innovation across the financial ecosystem.

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AI use in finance raises cyber fraud, systemic risks

AI’s integration into finance through new interfaces and growing dependence on third-party providers has widened the cyber risk landscape. Its reliance on large datasets, open systems, and automation creates entry points for adversarial attacks and data manipulation, threatening system integrity. A single breach can disrupt operations across multiple institutions and erode trust in AI, Rao pointed out.

The emergence of Generative AI further raises the stakes, enabling advanced fraud through deepfakes, fake identities, and AI-crafted phishing attempts.

“While AI is transforming many of the processes of financial institutions, the rise of Generative AI has also lowered the barriers for fraud, putting powerful deception tools in the hands of malicious actors. Deepfakes can mimic voices, faces, and documents with unsettling accuracy, while AI-generated phishing lures, fake identities, and forged credentials can slip past traditional checks,” Rao reiterated.

RBI’s annual report for FY2024–25 revealed a sharp rise in digital payment frauds, with 13,516 cases accounting for 56.5% of all reported banking frauds. However, fraud reported in a year could have occurred several years before the year of reporting.

“A critical vulnerability of AI is its potential to synchronize behaviors across the financial system. When institutions use similar models trained on overlapping data, their decisions on asset pricing, credit assessment, trading, and others may align, creating hidden linkages. This can amplify market stress, spread shocks rapidly, worsen liquidity shortages, increase asset price volatility, and trigger sharp, self-reinforcing market swings,” Rao added.

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RBI drafting broader guidelines to tackle AI model risks

The RBI deputy governor informed that the central bank is working on a broader Model Risk Management Guidelines to cover a wider range of models used across both operational and functional areas. Given the uneven adoption of technologies like AI across the sector and the diversity of entities involved, the guidelines will follow the principle of proportionality. The goal is to help regulated entities (REs) adopt suitable technologies while managing risks such as lack of explainability, algorithmic bias, and over-automation.

“As AI adoption gains traction, regulatory oversight is crucial in ensuring an efficient, responsible and fair adoption. Recognising the increasing usage of model-driven credit assessments and decision-making in REs, the RBI had issued a draft circular on model risk management in credit, setting out expectations on governance, validation, monitoring, and accountability,” Rao pointed out.

“Building on this foundation and recognising the increasing usage of models by the REs, not only for credit functions but also for wide spectrum of processes across functional and operational domains, the Bank is in the process of expanding the scope of these guidelines and would be issuing overarching Model Risk Management Guidelines applicable across all models. As technologies like AI are generally not adopted uniformly across the sector and owing to presence of a varied type of entities with different scales, the principle of proportionality has to be also factored in. The objective would be to ensure that all REs can adopt technologies best suited to their business models and customer needs, while effectively managing risks such as explainability, algorithmic bias, resilience, and over-automation,” he informed in his speech.

Emphasizing that “trust is the currency of banking”, the deputy governor pointed out that AI is transforming financial services and introducing a major shift impacting products, processes, and operations. The sector must adopt AI with foresight while balancing innovation with resilience through strong governance, diversified dependencies, and ongoing risk assessment. Ethical, transparent, and unbiased AI use is key to a sustainable future. This requires optimistic vigilance, which neither fears nor blindly embraces technology, but navigates it thoughtfully.

“Even as we broaden the credit coverage using algorithms and digital interfaces, maintaining the trust will be our biggest challenge and also our biggest responsibility,” Rao concluded.

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