Rewriting the Rulebook: How AI Is Modernising BFSI and Real Estate – Crypto News

A few years ago, a junior employee at a major Indian bank spent hours navigating outdated policy manuals just to respond to a client query. Today, that same task takes less than 30 seconds to complete. All thanks to an artificial intelligence (AI) powered chatbot. What once demanded tedious manual effort, multiple approvals, and endless email threads is now handled by intelligent automation, offering instant and reliable answers.

This offers a glimpse into a broader transformation quietly reshaping the banking, real estate, and financial services sectors. AI is no longer a distant promise. It’s here, and it’s redefining how legacy industries operate, deliver value, and make decisions.

One powerful example is the State Bank of India’s generative AI solution, askSBI. Designed to help employees tackle complex business scenarios, the chatbot serves as a centralised knowledge hub. It reduces dependence on manually curated documents, enhances internal communication, and streamlines access to critical information, ultimately improving operational efficiency and service quality across the board.

But AI in traditional sectors is not just about cutting costs or increasing speed—it’s about enabling smarter decisions, improving customer experiences, and future-proofing operations. This was the central theme of a recentpanel on Mint’s series All About AI featuring industry veterans such as Arundhati Bhattacharya, President and CEO of Salesforce – South Asia; Kripadyuti Sarkar, CIO at Ambuja Neotia; and Ratan Kumar Kesh, Executive Director and COO of Bandhan Bank. Together, they unpacked how AI is creating real, tangible change—and the roadblocks that still need to be overcome.

Watch the full episode below,

Banking: Smarter Services, Stronger Risk Management

In banking, AI tackles vast operational challenges swiftly and efficiently. Arundhati remarked, “Banking has a lot of operational challenges. There are risk management issues. There are issues of finding fraud. But more than anything else, there is an issue of giving a uniform level of customer service that really and truly gives an experience to a customer which will enable the customer to want to come back again.”

The transformation is already measurable. One of the most immediate and visible applications is fraud detection. Traditional systems rely on static, rule-based models that can be slow to adapt. In contrast, AI-powered tools continuously learn and evolve. For instance, in 2019, SBI deployed an AI-driven fraud analytics platform capable of analysing transaction patterns in real-time. Similarly, ICICI Bank’s AI chatbot, ‘iPal,’ utilises natural language processing not only to handle customer queries but also to proactively flag suspicious behaviour, thereby enhancing both customer service and fraud prevention. Besides these, multiple other banks, including HDFC Bank, Axis Bank, Federal Bank, and more, have implemented conversational AI solutions.

But AI’s role in banking goes far beyond risk mitigation. It’s helping banks deliver hyper-personalised experiences.

Ratan highlighted AI’s indispensable role in today’s financial services landscape, stating, “In the financial services industry, you cannot survive without AI. It provides a comprehensive view that allows effective management without needing a large number of experts on staff.”

The potential benefits of AI are substantial. According to anEY report, generative AI is projected to boost productivity in Indian financial services by 34–38% by 2030, with banking operations alone expected to see a 46% increase. Ratan also pointed to concrete improvements, noting, “Costs come down by 20 to 30%. Net Promoter Scores increase by 20 to 30%. Fraud detection, personalised customer offers, and credit decision-making — AI is fundamentally transforming the banking landscape.”

Further emphasising AI’s growing prominence, Gartner predicts that by 2026,90% of finance functions will have integrated at least one AI-enabled technology. This shift marks a move from experimentation to full operational adoption, making AI a core pillar of modern financial strategy and decision-making.

Real Estate: Faster Transactions, Smarter Investments

Kripadyutipointed out the inherent human dependency of the real estate sector. “Real estate is a human-dense industry. It has always been trusted human intelligence over any sort of intelligence, be it AI or ML.” He emphasised the challenges posed by human variability and emotion in decision-making, which led to difficulties in analytics and customer insights.

AI is helping real estate firms make data-backed decisions faster than ever before. From predicting market trends to qualifying leads and automating documentation, AI is streamlining everything.

One standout example of AI in action is the rise of intelligent property platforms, such asHousing.com. These platforms now use AI algorithms to analyse buyer behaviour, preferences, and search intent, enabling them to recommend properties more accurately and significantly reduce the time required to find a suitable home.

In the commercial real estate sector, the potential is even more transformative. By strategically embracing AI, companies can optimise everything from space utilisation and lease management to predictive maintenance and investment planning. According to JLL’s recent report, over 90% of C-suite leaders believe AI will fundamentally change the way the workforce operates within the next five years.

Kripadyuti highlighted the importance of transparency in real estate, describing it as “a business where I’m selling a dream to a customer,” who invests significant money in a property that is often just barren land at the time of purchase. AI helps provide real-time updates and data access to customers, enhancing trust. “AI gives us a platform that integrates my customer experience to that level where even a customer can have a photograph of the existing situation of the site. Not only photographs, if they want any of the financial data, their outstanding data, or even if they have any complaint, they can raise their concerns. This is real-time.”

Agentic AI: Enabling ‘Digital Labour’

Speaking of customer experience, one cannot overlook the developments in the field of agentic AI, which refers to autonomous systems capable of performing tasks, making decisions, and interacting in ways previously limited to humans. These intelligent agents are now redefining how industries manage scale, efficiency, and human connection.

“AI, especially the agentic autonomous layer, provides digital labour,” notes Arundhati. She explains it through an example of customer service. Traditional call centres often face long wait times due to staffing limits. Doubling the workforce to cut wait times isn’t feasible. “But with agentic AI, a 2-minute wait can be reduced to 2 seconds,” she says. This will dramatically improve customer satisfaction without the need for massive hiring.

Healthcare offers another compelling use case. Doctors are often overwhelmed with routine tasks like recording symptoms and drafting diagnoses. An agentic AI can handle these processes, freeing up the physician to “do the deep dive to give you a much more personalised human experience.”

Beyond automation, agentic AI unlocks time, giving employees the capacity to connect more deeply with customers and patients. “AI should help humans become more human,” Arundhati concludes, highlighting the technology’s role in shifting human roles from routine execution to empathetic engagement.

Transparency and Trust: The Common Thread

Both sectors underscore trust as a crucial factor. Sarkar pointed out that AI-enabled platforms deliver transparency, which is vital when customers invest their lifetime savings.

“Transparency to the customer, progress of the project to the customer is very important so that they can tie it up with the project,” he remarked.

The panelists agreed that investing in robust AI platforms like Salesforce is key to achieving this trust and future readiness.

Overcoming Barriers and Looking Ahead

India’s digital transformation is accelerating rapidly, though not without its challenges. Arundhati Bhattacharya acknowledged the regional dynamics, saying, “We are seeing signs in the East of a lot of interest; companies that want to go pan-India and have global ambitions. The entire ecosystem is changing.”

Ratan Kumar Kesh reflected on some of the initial hurdles, highlighting concerns around cloud security and legacy systems: “Initially, the lag was on account of the fear of cloud because India was basically an on-prem kind of an ecosystem. As we moved towards digitisation, there was a lot of technical debt.”

Despite these challenges, the momentum behind AI adoption is unstoppable. Kesh emphasised, “Change is not going to wait for them anymore. With the coming of generative AI, the future belongs to those who are agile.”