Like the health and hospitality sectors, banking too relies largely on human interactions and emotions. While the potential for generative AI in banking is remarkable, it may fail to judge human behaviour and falter, especially when offering tailored financial products and services to customers.
The McKinsey Global Institute (MGI) projects that GenAI could generate an additional $200-340 billion annually across the global banking sector, boosting industry revenues by 2.8 to 4.7 per cent mainly through enhanced productivity.
So, how are banks embracing AI?
In 2023, Accenture highlighted a transformative trend in Indian banking. It noted that with AI integration, banks can serve many customers simultaneously, boosting both transaction volume and digital interactions.
Back in 2017, HDFC Bank Ltd introduced India’s first AI-driven banking chatbot, Eva (Electronic Virtual Assistant). Powered by Bengaluru-based startup Senseforth, Eva was designed as a breakthrough in customer service, capable of handling millions of queries instantly across multiple platforms. Eva set the standard for AI-assisted customer interaction with the ability to pull information from thousands of sources and respond in under 0.4 seconds.
By 2020, ICICI Bank expanded on this concept with its chatbot, iPal, integrating it with Amazon Alexa and Google Assistant. This allowed retail banking customers to perform various transactions through simple voice commands. However, the service was discontinued in August 2021.
More recently, in 2023, the State Bank of India (SBI) announced a strategic AI-driven initiative aimed at enhancing decision-making and operational efficiency. With plans to deploy advanced data warehouses and data lakes, SBI is also exploring partnerships with fintech firms and non-banking financial companies (NBFCs) to revolutionise co-lending practices and drive a more connected financial ecosystem.
Meanwhile, Deutsche Bank’s innovative AI journey is powered by data approach, control, and talent. It has collaborated with Google Cloud (since 2020) and NVIDIA (from 2022), accelerating cloud transformation and AI adoption across the bank.
In 2023, it launched a bank-wide AI program with practical applications like AI chatbots, developer support tools, and unstructured data analysis, positioning it as an early adopter of generative AI and LLMs.
But is Everything Safe?
According to Kroll’s 2023 Fraud and Financial Crime Report, a survey of 400 senior executives across three continents revealed that 67% expect financial crime to rise in the coming year, with 57% identifying third-party gatekeepers as a key risk factor. The link between money laundering and organised crime is
significant, with up to $2 trillion (2-5% of global GDP) laundered annually as criminals work to mask illicit gains.
In response, banks are increasingly looking to AI.
For example, HSBC previously relied on rule-based systems to spot potential money laundering, often leading to numerous “false positives” that required manual review. Now, in partnership with Google Cloud, it uses advanced AI trained on extensive data to recognise suspicious activity autonomously.
This AI-driven Anti Money Laundering (AML) solution is more precise, reducing false positives and enhancing its detection capabilities without pre-set parameters.
As banks evolve their defences, fraudsters are getting more adept at bypassing them. Speaking at the DECODE webinar, Sahil Aneja, vice president at HDFC Bank, pointed out that traditional rule-based monitoring, though foundational, is rigid and struggles to keep up with the fraudsters’ methods.
“For example, when UPI first launched, there was a significant spike in fraud incidents. Banks responded by setting thresholds for unusual transactions, which temporarily reduced the scale and frequency of fraud. However, fraudsters soon adjusted to these rules, necessitating a shift to AI-driven platforms for better fraud detection,” he said.
Aneja spoke about a general benchmark in fraud prevention, indicating that within 30-45 days, fraudsters adapt and develop new methods to bypass platforms put in place by financial institutions. This means that institutions must continuously refine their systems, transitioning to self-learning models.
What’s Next?
Infosys Finacle, a part of EdgeVerve Systems, a fully-owned subsidiary of Infosys, recently introduced the Finacle Data and AI Suite, a powerful toolkit designed to bring AI seamlessly into banks’ digital operations and fast-track their AI journey. This suite provides a collection of platforms enabling banks to build low-code, predictive, and generative AI solutions from scratch, with a focus on transparency and explainability.
With this, banks can boost their data readiness, standardise AI model development, harness generative AI technologies, and deliver actionable insights across their entire ecosystem.
Meanwhile, Axis Bank believes that AI will not change the nature of work in India. However, the Mumbai-based firm has ramped up its technology team to 800 employees, up from about 60 nearly five years ago.
The bank currently employs about 70 people who work exclusively on AI and plans to further expand its team by 10% yearly. This shows that the adoption of AI in the banking industry is not slowing down.
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