Published 21:08 IST, January 31st 2025
Economic Survey 2025: Banks Using AI? Boon Or Bane?
While there are several uses and benefits of using AI in the banking system, there are some risks as well.

The Economic Survey of India was tabled in the Lok Sabha by the Finance Minister Nirmala Sitharaman on Friday and according to the survey over the past few decades, banks have consistently adapted the latest technological innovations to redefine customer interactions.
Ever since the boom of AI, the world is operating in an AI-powered digital age, driven by increasing data storage and processing costs, greater accessibility, and connectivity.
According to the survey, these innovations can lead to higher automation and often enhance human decision making speed and accuracy when correctly managed to mitigate risks.
Use Cases Of AI And ML In Banking In India
The use cases of AI and Machine Learning (ML) applications by banks in India range across areas such as credit underwriting, regulatory capital planning, liquidity management, fraud detection and prevention, risk assessment and management, portfolio optimisation, pricing models, and chatbots.
"The rapid pace of technological evolution in India, particularly in areas like AI, blockchain, and data analytics, has created new opportunities to reimagine traditional financial services and processes," the survey stated.
As per the survey, AI and large language models (LLMs) have enhanced customer service through interactive chatbots and personalised experiences, while blockchain offers secure, transparent, and efficient transactions.
Moreover, evolving consumer behaviour and expectations, driven by the rise of digital natives and increasing demand for personalised, seamless, and convenient financial solutions, encourage established companies and newcomers to innovate to remain competitive.
What Are The Risks?
While there are several uses and benefits of using AI in the banking system, there are some risks as well.
"The black-box nature of AI systems can make it difficult to assess the system's reliability or contest its decisions. This lack of transparency can lead to trust concerns and challenges in validating the fairness and accuracy of AI decisions, making it challenging to audit or interpret the algorithms that drive the decisions," the Economic Survey noted.
Accountability risks include difficulty in tracing decisions to their source and establishing liability.
Among other risks, there are risks related to human resources, such as inadequate human oversight, over-reliance on AI, and loss of human expertise; cyber-risks; malicious usages like synthetic identity frauds, rogue trading, and market manipulation; system related risks such as inability to intervene and market correlations; and third-party dependencies and service provider concentration.
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Updated 21:08 IST, January 31st 2025