About

India’s Financial sector is at the forefront of inclusion and innovation. Yet, as the industry scales, it faces increasing complexity: fragmented customer touchpoints, interconnected lending ecosystems, rising fraud risks, and evolving regulatory expectations.

Traditionally, financial institutions have relied on relational databases and rules-based systems, which store data in rows and columns. While these systems are effective for single-source reporting, they struggle to uncover insights hidden in the connections across customers, transactions, guarantors, co-lenders, and partners.

This is where knowledge graphs are emerging as game-changers. A knowledge graph creates a connected map of enterprise data, unifying information from KYC, transactions, collections, co-lending platforms, and even external watchlists. By making relationships first-class citizens, graphs enable financial institutions to:

• Detect fraud rings and synthetic identities that span multiple accounts or devices.
• Identify hidden credit exposures within MSME networks and promoter groups.
• Manage co-lending risks by visualizing interconnected exposures.
• Build a true 360-degree view of customers for personalization and compliance.

Globally, financial institutions are moving graphs from niche pilots to core infrastructure, because they allow leaders to ask ad-hoc, multi-source questions at speed and scale; questions that were previously impossible to answer. And they make your GenAI applications smarter.

The discussion today will explore why graphs are no longer optional, how early adopters are deploying them, and what it means for the sector’s future in resilience, compliance, and growth.