Agentic AI in financial services: How banks are rethinking lending, risk and decision-making

A loan application can involve multiple rounds of document verification, financial assessment, compliance checks and internal approvals before a decision is reached. For financial institutions handling thousands of applications, every manual handover adds time and operational effort. Agentic AI is beginning to change how these processes work by enabling systems to coordinate tasks, analyse information and advance workflows with less human intervention. This is particularly relevant to lending, where the quality and speed of a decision can influence both customer experience and portfolio performance.

Where Agenetic AI is changing financial services

One of the clearest applications lies in loan processing. AI systems can extract information from bank statements, tax records and financial documents, reconcile data across sources, identify missing information and prepare files for assessment. DBS Bank India, for instance, has deployed AI-powered document processing to extract and validate data from loan files, KYC forms and financial statements, reducing manual work involved in preparing these documents for further processing. The bank is also developing AI-driven underwriting capabilities for MSME lending.


For lenders, the opportunity extends beyond faster paperwork. AI agents can support credit assessment by bringing together financial records, repayment histories and other relevant information, helping credit teams identify risks and evaluate applications more efficiently. Human reviewers can remain responsible for consequential lending decisions, particularly where cases are complex, or the available information requires judgement.

Fraud prevention and compliance offer another area of application. Financial institutions can use AI to examine transaction patterns, flag anomalies, monitor changing risk signals and route suspicious activity for investigation.

Agentic systems can also help compliance teams reconcile documents, track obligations and identify exceptions that require attention. These capabilities become particularly valuable when institutions must process large volumes of transactions while maintaining consistent oversight. The opportunity is extending across the lending lifecycle, too. Indian non-banking financial companies are incorporating AI into customer acquisition, underwriting, servicing and collections, using data analysis and automation to improve efficiency and risk management.

The challenge of scaling autonomous decisions

As AI systems take on more complex tasks, financial institutions must determine which decisions can be automated, which require human approval, and how every action can be audited. Data quality, explainability, customer privacy and accountability become central to implementation, particularly when decisions affect credit access or financial security. The Reserve Bank of India’s regulatory environment also makes sound governance essential to responsible deployment.

The challenge for established banks is to introduce greater speed without weakening the controls that protect customers and the financial system. For newer fintechs, the opportunity lies in building agile operating models while establishing the reliability required to scale.

From AI experimentation to financial services transformation

These questions will take centre stage at the AI Business Transformation Masterclass & Awards 2026, presented by The Economic Times in association with IIM Bangalore.

The programme will examine Agentic AI in Financial Services & Lending, with a focus on how legacy enterprises and new-age disruptors are approaching autonomous decision-making, compliance at scale and practical AI deployment. The discussions will explore how financial institutions can move beyond isolated use cases to build systems that deliver measurable business outcomes.

Taking place on 19 November 2026 at IIM Bangalore, the Masterclass will bring enterprise AI implementation into focus through practical discussions, live demonstrations and use cases across industries. The day will also feature the ET Most Innovative AI Product Awards 2026, recognising AI innovations creating meaningful business value.

For financial services leaders, the next stage of AI adoption will depend on how effectively they combine faster decisions with sound risk management. The institutions that get this balance right will be better positioned to scale AI across the lending value chain and build more responsive financial operations.

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