India’s financial services sector stands at a pivotal moment. With AI poised to redefine how institutions operate and serve customers, the ability to harness the true potential of Generative AI (GenAI) hinges on a bank’s underlying infrastructure. Yet, many institutions remain tethered to their legacy systems, which will make the rapid adaptability required to succeed in the new AI era, not just difficult but impossible. In this context, modernisation is not just an option, but a strategic imperative.
A recent industry report suggests that GenAI could boost productivity in Indian banking operations by up to 46% by 2030. Already, 74% of financial institutions have initiated GenAI proof-of-concepts, and 42% are investing dedicated budgets to scale these technologies. But enthusiasm alone is not enough—without the right infrastructure in place, these initiatives risk stalling at the experimentation stage. Legacy applications, built on rigid architectural foundations, cannot support the flexible data layer and agile workflows AI requires to deliver transformative results.
Legacy Infrastructure: The Biggest Roadblock
Despite growing excitement around GenAI, most banks rely on outdated core systems unfit for real-time data processing or intelligent automation. These legacy platforms are fundamentally at odds with the data-hungry, agile nature of GenAI models.
Moreover, data silos across departments and fragmented formats further obstruct the flow of usable information, severely limiting the performance of AI tools. Without unified, high-quality, and accessible data, GenAI cannot deliver the accuracy, personalisation, or predictive capabilities it promises. This technical debt inflates risk, slows innovation, and increases the cost of experimentation, a problem Indian banks cannot afford in the fast-moving AI landscape.
Modernisation as a Strategic Enabler
To truly operationalise AI, banks must first rebuild their technological foundation. This means moving away from monolithic, on-premise systems toward modern, modular, and cloud-based architectures, and often, microservices. These platforms not only support scalable computing and real-time analytics but also better enable seamless integration of AI workflows.
Unlike traditional “lift and shift” migrations, true modernisation should offer banks a transformative opportunity – to strategically re-architect outdated systems rather than simply migrating technical debt to another location. This holistic approach empowers financial institutions to eliminate inefficiencies and create a foundation for continuous innovation.
By consolidating structured and unstructured data into unified, accessible formats, banks can feed GenAI models with the information they need to deliver actionable insights. This shift is already underway—many financial institutions are hollowing the core, building fast experience layers, adopting microservices, flexible data models, and event-driven architectures to better support AI-driven innovation.
AI-powered modernisation
GenAI isn’t just a factor making modernisation more urgent; it’s also a way of making modernisation possible. Previously, modernisation was expensive, high-risk, and often took years. Even then, companies would often still end up using relational databases unsuited for modern use cases.
When combined with the right strategy and talent, the rise of intelligent, AI-powered automation eliminates much of this complexity, helping banks accelerate their modernisation journey by up to 3x at a fraction of the cost, while reducing risk. In fact, leading banks worldwide have seen critical tasks, like code migration and regression testing, accelerated by up to 10x using AI-driven approaches.
One example comes from Chennai-based Intellect Design Arena, which has begun modernising the legacy infrastructure underpinning its Wealth Management platform using MongoDB and GenAI tooling. Phase one of the migration has demonstrated remarkable results:
- Development transformation cycles were completed up to 200% faster, highlighting the effectiveness of automating processes that were previously resource-intensive.
- By reducing the overall duration of the customer onboarding workflow by 85%, clients can now access critical portfolio insights significantly faster, accelerating both decision-making and investment outcomes.
- Transaction processing times saw substantial improvement, enabling the platform to seamlessly support large-scale operations for new clients without delays.
This modernisation not only made their systems more AI-ready but also enabled them to launch new features for global banking clients in weeks instead of months.
Intellect’s success underscores how GenAI can serve as a true catalyst—not just for enhancing customer experiences but for rebuilding the very foundations of financial technology. Their journey demonstrates that with the right data strategy and scalable platform, banks and fintechs can overcome legacy challenges and bring GenAI-powered innovations to market at unprecedented speed.
Spotlight on India
India is uniquely positioned to accelerate this shift. A digital-first population, a thriving fintech ecosystem, and the presence of national platforms like IndiaStack and UPI offer a strong springboard for transformation. Forward-thinking banks are already adopting cloud-based solutions and modern data platforms to reduce reliance on legacy infrastructure—ensuring AI tools can be deployed with speed and scale.
This modular approach to modernisation not only minimises disruption but also allows banks to iterate faster and with greater resilience, ensuring long-term scalability and adaptability. Organisations that embrace modernisation today will unlock capabilities that position them to set the pace for AI-driven innovation across the financial sector.
Regulatory and Organisational Readiness
However, technology alone won’t suffice. To sustain AI-led modernisation, banks must develop robust internal governance and regulatory frameworks. India’s regulators are increasingly emphasising AI explainability, data transparency, and responsible innovation—a sign that the compliance environment is evolving in step with technology.
Internally, banks must upskill teams, invest in data science talent, foster a culture that encourages experimentation and cross-functional collaboration, and build mindshare to adopt modern infrastructure, data platform, and other technologies.
Conclusion: Modernisation First, AI Next
GenAI and legacy modernisation are not parallel paths—they are deeply intertwined. Without modernisation, GenAI will remain underutilised, limited by outdated systems that cannot support its full potential. As India aims to become a $30 trillion economy by 2047, the BFSI sector must act swiftly to embrace modernisation that fuels AI’s true potential.
The best modernisation strategies address architectural challenges from the ground up while using AI itself to make the process faster, cheaper, and more predictable. As demonstrated by Intellect Design’s results, the right data strategy and scalable platform can propel banks toward AI-powered transformation with unprecedented speed and success.
Views expressed by: Vinay Agarwal, Manager, Solutions Architecture, MongoDB
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