Unleashing the next storm of BFSI transformation with Generative AI

Jan 11, 2024
Banking | 5 min READ
While opportunities abound, organizations must identify the challenges early on and dispel key myths surrounding a powerful technology to foster meaningful adoption to do what is right and not just what is taught.
The Banking, Financial Services, and Insurance (BFSI) sector is expected to be the highest beneficiary across industries from the advent of GenAI, as the technology will deliver $200-340 billion worth of value creation to the industry. However, all organizations and industries 1 will derive these benefits through various use cases.
Ravi Chauhan
Ravi Chauhan

Delivery Head,



More importantly, success will strongly depend on formulating viable and high-value use cases, steering clear of the risks, and setting the right expectations early on. Consider the following three checkpoints that will mark the GenAI adoption journey of every BFSI organization in the coming years – i.e., high-value use cases of the technology, key risk factors that could hamper adoption success, and myths to debunk before starting your GenAI adoption journey.
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Where are the opportunities?
Key use cases of GenAI in BFSI
Personalization attains a new meaning in the CX vocabulary
GenAI will personalize services for BFSI customers to a level never seen before. In the coming years, customers will interact with GenAI-powered virtual agents in real time. Unlike hard-coded AI assistants of today, these virtual agents will deliver human-like problem-solving on call without long waits and carry out empathetic conversations with users.
Moreover, personalization will also come in the form of natural language summaries of customers' financial activity through apps tailored to individual users' needs and marketing messages that truly appeal to each customer, addressing their specific unique needs.
Faster loan disbursal processes, turbocharged by GenAI
A significant number of bottlenecks, right from loan form submission through any means to the disbursal process, can be eliminated with GenAI. These include recommending the right loan product, identity verification, summarization of credit scores, and customer onboarding.
With GenAI, the loan disbursal process will become even faster than today, enabling financial institutions to disburse loans to customers on the same day. This will lower the cost per application, maximize loan portfolio growth, and improve liquidity management outcomes.
Advancing trust and security with blockchain-GenAI integration
While blockchain is already enhancing the transparency of operations of BFSI companies, integrating this technology with GenAI will significantly advance the trust and safety of the digital architectures underpinning modern banking and financial services.
Besides many other offerings, smart contracts created by GenAI and complex financial agreements can be executed faster, and existing AI fraud detection techniques can be bolstered against more advanced fraudulent activities with generative adversarial networks (GANs).
Maximizing operational efficiency with GenAI and automation
Today, several processes that humans execute are prone to errors. These include data capture, document verification, and document-intensive risk and legal workflows.
GenAI’s text processing and analysis capabilities will significantly improve the accuracy with which these processes are orchestrated at BFSI organizations. This will maximize operational efficiency and lower the cost of operations as the human workforce shifts their focus to more value-added tasks.
A multi-faceted impact on the cryptocurrency and NFT ecosystem
The cryptocurrency and NFT environment will experience a disruptive impact of GenAI technologies, most significantly in the following areas:
  • GenAI algorithms will enable personalized financial products across DeFi platforms by analyzing user data.
  • The velocity of content creation will rise in Non-Fungible Tokens (NFT) marketplaces, impacting the dynamic of value creation in these ecosystems.
  • GenAI algorithms will be leveraged to analyze and spot vulnerabilities in smart contracts, leading to more secure and robust decentralized apps.
Risk factors to consider before adopting GenAI in BFSI
Like all AI, GenAI is prone to the risk of bias
One of the most significant risk factors of GenAI adoption is the risk of biased outcomes. AI models have been found to deny mortgages and issue expensive insurance coverages to people from marginal communities, even in cases where an algorithm was trained to ignore features pertaining to them. However, sophisticated algorithms like GenAI may discover correlations from non-protected features, which can cause bias to creep into the output. This can not only lead to unfair practices but also attract regulatory action.
Proprietary models could be black-box algorithms, which makes bias mitigation even more challenging. Therefore, sophisticated bias mitigation techniques are essential to safely deploy GenAI solutions in the BFSI industry.
GenAI adoption from an industry perspective
As GenAI solutions become readily available, multiple banking organizations may adopt them for use cases like securities trading. This may amplify the market's volatility and cause systemic risks as various organizations execute correlated trading strategies. While regulators are likely to issue statutes to address such risks, organizations must factor such risks into their adoption strategies.
GenAI isn’t a silver bullet
Debunking myths surrounding GenAI capabilities
#1. One size doesn’t fit all
Each financial services organization is different in the customers it caters to, the services it sells, and the operating model it employs. The same set of processes might need additional training datasets and models for different clientele across the same organization to bring about focused personalization. GenAI use cases must be piloted and refined for these deployment scenarios, and models must be fine-tuned to meet the organization's specific needs.
#2. Not a quick-fix solution
GenAI offers a valuable edge in some use cases – like the ones listed above. However, it is not a quick-fix solution for all problems. For instance, deploying the technology through a chatbot will not magically solve all customer experience issues – especially if the pain points lie elsewhere. It's a long-term strategy with initial effort into training on the exact need and ensuring statutory laws are abided by.
#3. Nothing is 100% accurate
Like all AI algorithms, GenAI is prone to errors, too. While a newly deployed model will be reasonably accurate, its accuracy will climb over time. Nonetheless, it is prudent to employ GenAI and a human-in-the-loop to mitigate the consequences of inaccurate outputs. Some regulations also necessitate such frameworks in the use of AI.
#4. GenAI isn’t a cold robot
Unlike early hard-coded robotic process automation (RPA) systems, GenAI doesn’t operate like a cold, indifferent algorithm. It may not bring the emotional quotient of a human being, but recent advancements bring emotional intelligence capabilities to GenAI. A leading example is Google’s Gemini 1.0, which can infer the tone, mood, or emotion in voice, text, or video streams.
Next steps
The opportunities unlocked by GenAI in BFSI are numerous. As the industry adopts high-value use cases, GenAI will bring another wave of revolution to the BFSI industry, much like the rapid digitization that unfolded throughout the pandemic.
While the scope of impact of GenAI on BFSI institutions is unarguably vast, the question is how extensive this change will be. Could GenAI enable economies to predict recession events and, more importantly, figure out ways to mitigate them beforehand?
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