.png){kind=logo}
For nearly a decade, business success was closely associated with rapid growth, market expansion, hiring, and investor confidence. But as the business environment becomes more complex and expensive, companies are placing greater emphasis on making smarter financial decisions.
This shift is particularly important in banking and financial services, where digital adoption has created massive amounts of financial data. AI is now creating new ways for institutions to use that data to personalize services, automate workflows, improve lending decisions, and strengthen financial intelligence.
In this episode of the Analytics Insight Podcast, Priya Dayalani speaks with Nirav Prajapati, Founder and CEO of Ignosis, about how AI, financial data, and digital infrastructure are reshaping the future of banking and financial services. The conversation also explored India's digital public infrastructure, consent-driven data sharing, and the investments banks need to make to become truly intelligence-driven organizations.
Here are the excerpts from the interview:
Digital banking has already transformed how customers transact and access financial services. According to Nirav Prajapati, the next stage is about using the large digital footprint created by these transactions to deliver more personalized financial services.
The growth of UPI, banking applications, e-commerce, and other digital channels has generated significant amounts of financial data. With generative AI and newer AI models, financial institutions can use this data, with customer consent, to provide more personalized recommendations.
This could include recommendations around insurance, credit cards, loans, investments, or other financial products. AI assistants could also help customers manage spending, simplify their finances, and make more informed financial decisions.
The transition, therefore, is moving from simply providing digital access to using data and intelligence to create more personalized financial experiences. Pasted text
Lending has traditionally depended heavily on credit bureau information and historical repayment behavior. But account aggregator-based consented data sharing is creating access to additional financial information.
This can help lenders understand cash flows, business income, expenses, and other financial activity. It can also provide new-to-credit customers, gig workers, and small businesses with a way to share their banking information more easily.
According to Prajapati, better lending decisions depend on having the right data from trusted sources and being able to interpret it effectively. Identity insights, credit information, income, and financial obligations can then be combined to support the lender's decision-making process.
AI and automation can handle an increasing share of these processes, while human judgment remains important for more complex cases. Pasted text
One of the immediate applications Prajapati highlighted is voice AI. Banks and financial institutions handle large volumes of calls related to product information, customer support, financial journeys, and payment collections.
Voice AI can support these interactions at scale, including helping customers understand products or reminding them about missed payments.
Workflow automation is another important area. During lending processes, for example, AI can help collect documents, extract information, verify details, cross-check them against policies, and pass the relevant information into credit underwriting.
AI is also being used in fraud detection, where models can analyze large datasets and identify patterns associated with potentially fraudulent activity.
Together, these applications show how AI is already expanding across lending, voice interactions, compliance, fraud detection, and other banking functions. Pasted text
The account aggregator framework is creating a different model for how customers share financial information with institutions.
Previously, customers might have had to download bank statements, print them, or manually submit them to a lender. With account aggregators, customers can provide consent and share their financial data digitally.
Prajapati explained that customers can see whom they are sharing their data with and the purpose for which it is being shared. They can also revoke consent when they no longer want to continue sharing the information.
This model gives customers greater control over their financial data while allowing financial institutions to access information more efficiently for services such as lending. Pasted text
India's digital ecosystem is becoming an important foundation for the next stage of financial services.
Prajapati pointed to digital public infrastructure such as Aadhaar, UPI, Account Aggregator, and ONDC, which operate at a population scale. While India may not have built the world's largest AI models, he argued that the country has developed digital infrastructure capable of supporting large-scale data-driven services.
This existing digital footprint can provide an important foundation for AI adoption and personalized financial services. Pasted text
For banks looking to expand their use of AI, Prajapati identified three important areas: data, talent, and governance.
The first is building a strong data foundation. Banks need to clean, organize, and structure their enterprise data because the quality of the underlying data directly affects how effectively AI models can perform.
The second is talent. AI tools are increasingly being used across engineering, product, sales, and other business functions. Training employees can help organizations improve productivity and ensure that teams are better prepared to work with these tools.
The third area is governance and compliance. Automated systems can identify standard compliance and governance issues, allowing human oversight to focus on more critical cases.
Together, these investments can help banks move beyond digital operations toward more intelligence-driven decision-making. Pasted text
Looking ahead, Prajapati believes the leading financial institutions will not necessarily be those with the largest branch networks or the most employees.
Instead, competitive advantage will increasingly depend on how effectively institutions use AI agents, automate workflows, improve feedback loops, adapt pricing, and accelerate decision-making.
Continuous underwriting, for example, could allow policies and decisions to evolve based on new feedback. At the same time, organizations will need to determine where human expertise remains necessary and establish guardrails to ensure AI operates as intended.
The competitive advantage of the next decade, according to Prajapati, will come from the ability to adapt to AI, act quickly, and build effective feedback loops. Pasted text
Listen to the full discussion on the Analytics Insight Podcast.
