By Arvind Subramanian, Managing Director India & EVP – Global Enterprise Strategy & Incubation at Iron Mountain
India’s banking, financial services and insurance (BFSI) sector is entering a new phase of AI adoption, moving beyond experimentation towards systems capable of executing and orchestrating complex workflows. This shift is particularly relevant to back-office operations, where large volumes of data, regulatory requirements, repetitive processes and legacy systems continue to create operational challenges.
In India, the opportunity is amplified by the rapid digitisation of financial services leveraging platforms such as Aadhaar, UPI, Account Aggregator and the Open Credit Enablement Network (OCEN), which have transformed how institutions manage customer interactions, transactions and compliance.
However, the potential of agentic AI depends on more than the sophistication of the technology. Its effectiveness ultimately rests on access to high-quality, trusted and well-governed data.
The advent of agentic AI in BFSI back offices
Across India’s BFSI sector, back-office functions that have traditionally relied on manual processing and rules-based automation are being reimagined through agentic AI. Unlike traditional automation, AI agents can coordinate multiple steps across systems, interpret structured and unstructured information, and take action within defined boundaries. Human oversight remains an important part of the process where required.
In India, the emergence of agentic AI is set to transform BFSI back-office operations by building on the country’s growing Digital Public Infrastructure (DPI) ecosystem. As banks, insurers and financial institutions increasingly leverage Aadhaar, UPI and Account Aggregator, agentic AI can help streamline activities across KYC and AML, lending, underwriting and insurance claims. It can gather and validate information from multiple sources, identify discrepancies and move workflows forward more efficiently. For Indian institutions, this creates opportunities to improve operational efficiency, accelerate decision-making and enhance customer experience across growing volumes of data and documentation.
The importance of data quality
Years of siloed systems, inconsistent records and fragmented digital and physical repositories have created mountains of “dark data.” When critical context remains trapped in unstructured documents, emails, or legacy systems, AI agents cannot operate reliably or safely.
This challenge is especially pronounced in India, where many BFSI organisations continue to operate across a mix of modern digital platforms and decades-old legacy systems, alongside extensive archives of physical customer and compliance records. As institutions seek to deploy AI across branches, service centres and distributed operations, creating a unified and trusted data layer becomes a strategic priority rather than a technology exercise.
A recent study by Iron Mountain found that 81% of BFSI leaders expect AI agents to eventually handle more than 75% of routine back-office tasks, yet only 13% have successfully scaled such deployments. This gap highlights a critical reality: while confidence in agentic AI is high, success ultimately depends on data quality, process consistency and governance.
The Operational Roadmap
Turning agentic AI into measurable business value requires BFSI organisations to focus on high-volume, manual-intensive workflows where automation can deliver clear outcomes. This starts with building AI-ready data foundations by digitising and classifying information, eliminating redundant data and connecting records across core banking or insurance platforms, document repositories and other systems. With trusted and accessible data in place, AI agents can work more effectively across workflows, reducing operational friction and improving speed and accuracy.
For Indian BFSI institutions, the opportunity is particularly significant as they support a rapidly growing digital customer base while navigating increasing competition from banks, NBFCs, insurers and fintechs. Success should ultimately be measured through business outcomes such as faster onboarding and lending decisions, improved processing accuracy, stronger compliance and fraud controls, reduced risk and a lower cost to serve, rather than simply the number of tasks automated.
Governance & Control
As BFSI organisations move from robotic process automation to agentic AI, strong governance and human oversight will be critical to building trust and supporting responsible adoption. AI agents should operate within clearly defined regulatory and organisational guardrails, with appropriate access controls, audit trails and accountability mechanisms built into workflows from the outset.
This is particularly important in India, where regulators are placing greater emphasis on responsible AI, customer protection, model accountability and compliance with evolving data privacy requirements, including the Digital Personal Data Protection (DPDP) Act and in the case of financial institutions, the Reserve Bank of India’s Data Governance Framework (DGF). Transparent and explainable systems, supported by human judgement for exceptions and critical decisions, can help financial institutions scale AI while maintaining control and stakeholder confidence.
Reimagining BFSI back offices with agentic AI
Agentic AI has the potential to transform BFSI back offices into intelligent, increasingly autonomous operating environments. By automating routine activities, orchestrating workflows and supporting real-time decision-making, organisations can unlock greater speed, accuracy and operational resilience. Realising this potential, however, requires strong data foundations, connected information ecosystems and modernised operating environments.
For India, the opportunity extends beyond operational efficiency. As financial institutions serve a rapidly expanding digital economy and a diverse customer base across geographies and segments, agentic AI can help scale operations, improve service delivery and expand access to financial services without a corresponding increase in operational complexity. India is uniquely positioned to benefit from this shift, supported by its rapidly evolving digital financial ecosystem and continued focus on innovation. Organisations that combine trusted data, effective governance and workforce readiness will be best placed to harness the full potential of agentic AI and build more intelligent, resilient and scalable BFSI operations for the future.