India’s financial services sector presents a study in contrasts. From a digital infrastructure standpoint, we are among the most progressive economies in the world. For example, UPI processed 21.63 billion transactions in December 2025 alone, a number that would have seemed implausible a decade ago. The problem arises when customers require assistance; generally, they are referred to a contact centre that has not kept pace with advancements in technology or the evolution of brand voice. The disparity between digital transaction efficiency and service experience is where customer confidence gets tested.
The experience gap that digitalisation has created
The digital-first approach of BFSI has enhanced transactions but has not enhanced conversations with their customers. Customers can open new accounts, apply for a loan, and transfer money in seconds, but if there is a problem, such as a transaction not being completed, a charge being incorrect or suspected fraud, it is at this point that the experience will fall apart; the contact centre will be the weakest point in an otherwise smooth customer journey, and it will be at this point that customers’ confidence will either be reinforced or diminished. Financial institutions do not generally measure the customer experience with the same level of diligence as they do for successful transactions.
Most financial institutions measure their digital channels with rigorous precision. Few apply the same discipline to what happens when a customer picks up the phone.
The challenges facing BFSI when using legacy contact centre models
The challenges facing BFSI with respect to their legacy contact centre model are structural in nature. The compliance complexity associated with providing service via a legacy contact centre is far more significant than the compliance complexity associated with providing service via digital channels. Each and every interaction must be regulated and recorded; therefore, there are strict regulatory requirements associated with the handling of consumer data (privacy, consent, audit trails, etc.), making the provision of services through a contact centre much more lengthy, cumbersome and inefficient than providing services through digital channels. Operational challenges are created for businesses due to the wide diversity in India. For example, customers are located in urban or rural locations across the country, they speak numerous languages, and they have varying degrees of financial literacy. There cannot be a single contact centre model applied across all of these types of customers thus producing the same outcome. In addition, the sheer size of the operations presents another challenge. A function such as collections, KYC verification, loan servicing and fraud alerts will operate at such a high level that human only based systems struggle to deliver both speed and quality.
What can change with the introduction of AI
AI does not solve everything, and institutions that approach it with unrealistic expectations will be disappointed. But it solves specific, well-defined problems remarkably well.
For high-volume, routine interactions – payment reminders, EMI follow-ups, account status queries, verification calls – AI delivers consistency and scale that human agents structurally cannot. It logs every interaction in real time, captures consent automatically, and flags anomalies the moment they appear. This is not merely an operational efficiency; it is a compliance advantage that becomes increasingly significant as regulatory scrutiny intensifies.
AI also enables a quality of personalisation that is genuinely difficult to achieve at scale with human agents. Access to a customer’s complete interaction history, preferred language, risk profile, and transaction behaviour – processed in real time, allows for communication that is contextually relevant rather than generically scripted.
What AI does not replace is human judgement. Complex grievance resolution, sensitive financial conversations, high-value negotiations. These require empathy and contextual reasoning that current AI cannot replicate. The right model is not AI instead of humans. It is AI handling what it does better, so humans can focus on what only they can do.
The Challenge of Implementing AI in India
Most of the AI models that were developed internationally do not work in India due to cultural and environmental differences. Customers switch languages while on the phone and there is background noise which is very peculiar to India. All this is part and parcel of Indian life and AI models that are designed for the reality of the Indian environment will have a better chance of success as compared to general purpose models available out there.
Trust and Regulation
As digital transformation continues to grow, so does the risk of financial crime. In 2024-25, over 13 lakh UPI fraud incidents were reported, resulting in a loss greater than ₹1,000 crore. With fraud on the rise, AI technology plays a key role, not only in creating operational efficiencies, but most importantly, establishing trust with the consumer. Continuous monitoring, real-time detection of accretions and a full audit trail will provide protection for the customer as well as compliance with the regulatory authorities. This positions AI as a compliance enabling technology, as opposed to merely operational technology.
How to Successfully Implement AI
In order to successfully implement AI, the institutions and organizations that succeed will treat AI as “core” infrastructure, rather than simply as an “add on”. The question is no longer, “Should we implement AI into our systems?”, but rather, “What should we build our systems around in order to drive the next decade of financial services?” There is a significant change in the type of metrics that the organizations use to evaluate performance. Rather than measuring cost per call, organizations are using cost per resolution and quality of outcome metrics, since this demonstrates greater care for the customer experience than for operational efficiencies alone. The future operating model, with respect to AI, is a combination of Human and Machine Systems, where AI assists/hands off routine type of interactions and the Human provides focus and service for higher value and more complex interactions; where over time with iterative improvements, both Human and Machine will improve and work better together.
The ultimate measure of AI in financial services is not an efficiency ratio. It is whether a customer in Jaipur or Jorhat, calling about a problem that matters to them, feels heard, understood, and served with dignity. That has always been what financial services is about. AI, applied thoughtfully, makes it possible at a scale that was previously unimaginable.
