Artificial intelligence is moving beyond chatbots and back-office automation in India’s banking sector, with lenders using it across customer service, loan processing, fraud detection and risk management. But as AI takes on more decision-making functions, banks will also need stronger safeguards around data privacy, explainability and human oversight.

A recent report by Boston Consulting Group (BCG), the Federation of Indian Chambers of Commerce and Industry (FICCI) and the Indian Banks’ Association (IBA) said Indian banks need to move AI from pilots into their core operating models.

The report identified productivity, credit affordability and risk management as three areas where AI could have a significant impact.

Industry experts see the same shift taking place, although they point to trust and governance as important conditions for wider adoption.

According to Suman Bhowmick, Founder & CEO, Idea Simplified, an innovation-driven event solutions and brand consulting company, AI can make customer interactions faster and more personalised, while multilingual tools could improve access to financial services. In lending, AI can assess a wider set of financial signals and potentially help institutions serve customers with limited traditional credit histories.

The technology is also changing how banks approach fraud.

Bhowmick said emerging threats such as deepfakes, synthetic identities and mule accounts require systems that can identify unusual patterns in near real time.

From digitalisation to prediction

The shift is also visible in risk management. Earlier technology waves largely digitised existing processes, while AI is being used to identify patterns and predict potential problems.

“Unlike earlier technologies that simply digitised processes, AI is helping banks make faster, smarter, and more informed decisions,” said Prasanna Lohar, President, India Finance Network (IFN) and Founder, Finnovation Lab,
a private business consulting, innovation, and incubation platform.

Lohar said AI can help lenders look beyond conventional credit scores and assess cash flows and financial behaviour, potentially widening access for MSMEs, gig workers and first-time borrowers.

He also pointed to the combination of AI with India’s digital public infrastructure, including Aadhaar, UPI and the Account Aggregator framework, as a potential enabler of more data-driven financial services.

For banks, the attraction is not only faster processing but earlier intervention. AI-based systems can identify unusual transactions, changing repayment behaviour or other warning signals before they develop into larger problems.

Priyanka Agrawal, Co-Founder & COO, AppSquadz, a global IT consulting and software development company, said this marks a change from earlier digitalisation efforts.

“Previous waves of digitisation … made the existing processes faster, but what AI is doing now is fundamentally questioning whether those processes needed to exist in the form they did at all,” she said.

Agrawal said AI-based anomaly detection can continuously identify deviations from normal behaviour, while predictive tools can provide early signals on borrower stress and portfolio risks.

Lending could see one of the biggest changes

Loan origination is emerging as one of the clearest use cases.

AI can combine document processing, KYC verification, risk categorisation and other checks that were earlier handled as separate steps.

Yatin Pednekar, Co-founder & Chief of Products, Mobicule Technologies, a Mumbai-based fintech and software company, said AI is helping lenders move from manual, multi-step processes towards faster digital origination.

“Loan origination that used to take days … can now happen in minutes because AI is handling risk categorisation, document intelligence, and KYC verification at the same time rather than as separate manual steps,” he said.

Srijan Nagar, Co-founder, FinBox, an enterprise digital lending and credit infrastructure platform, said AI agents are also changing how borrowers interact with lenders.

Applications can be completed through channels such as WhatsApp, chat or voice, while systems can classify and cross-check documents as they are submitted.

This could be particularly relevant for first-time borrowers and customers who may find traditional application processes cumbersome.

AI can also follow up on incomplete applications, identify missing documents and enable remote KYC and e-sign processes.

However, faster approvals do not automatically mean easier or cheaper credit.

The BCG-FICCI-IBA report noted that operating and collection costs account for 40-50% of the cost to serve, particularly making affordability a challenge for small-ticket products.

AI could reduce some of these costs, but whether the resulting efficiency gains translate into lower borrowing costs for customers will depend on how lenders use those savings.

The trust question

As AI becomes more involved in lending and fraud decisions, the technology also raises a different set of risks.

A loan rejection, fraud alert or account restriction can have a direct financial impact on a customer. Experts therefore argue that customers should not be left with an opaque algorithm when such decisions are made.

Bhowmick said institutions need to be able to explain important AI-driven decisions, test systems for bias and provide a mechanism for human review.

“Responsible AI must be built into financial services from the beginning, not added later,” he said.

Agrawal similarly warned that historical financial data can contain evidence of past exclusion. If AI models learn from that data without adequate testing, they could reproduce existing biases rather than improve financial inclusion.

Data privacy is another concern because AI systems can process highly sensitive information, including transaction histories, repayment behaviour and other behavioural signals.

Pednekar said consent, audit trails and explainability should therefore be treated as core product requirements rather than compliance measures added after deployment.

Nagar said the greater autonomy given to AI systems also requires stronger controls. He identified auditability, explainability and human sign-off for high-risk decisions as key safeguards, particularly when AI is used in lending.

AI may change banking, but not remove accountability

The emerging picture is therefore less about replacing human decision-makers and more about changing where human expertise is applied.

AI can automate repetitive work, identify patterns faster and bring together information that would otherwise take considerable time to process. But decisions involving credit, customer funds and financial access still require accountability.

For Indian banks, that could make the next phase of AI adoption a balancing act: extracting productivity gains and expanding access while ensuring that faster, automated decisions remain transparent, secure and subject to human oversight.