Graph LLMs: The Next Frontier in AI forBanking, Financial Services, and Insurance

Written by: Chandramouli Pandya from IFF.

The Indian banking, financial services, and insurance (BFSI) sector stands at the epicenter of an unprecedented AI transformation.

The banks that embrace AI today won't just survive tomorrow, they will define it.

Recent data reveals that 74% of Indian financial firms have initiated Generative AI proof-of-concept projects, with 11% already running in production. This isn't merely adoption; it's a fundamental reimagining of how financial services operate.

The productivity gains are staggering. EY research indicates that GenAI is poised to improve productivity levels in Indian financial services by up to 46% by 2030. Private banks are leading this charge, they are six times more likely to discuss AI initiatives in their annual reports compared to 2015-16. Companies like Bajaj Finance have already demonstrated tangible returns, saving ¹150 crore annually through AI-driven bots for customer care, sales, and onboarding.

The Reserve Bank of India has recognized this transformative potential, preparing a framework for responsibleand ethical adoption of AI in the financial sector for 2025-26. With over 13,000 cases of bank fraud reported in FY 2022-23 largely due to rapid digital growth, the urgency for sophisticated AI solutions has never been greater.

Understanding Graph LLMs: Where Relationships Meet Intelligence

In the data economy, relationships aren't just connections, they are competitive advantages waiting to be discovered. Traditional AI models treat data points in isolation, but financial services thrive on relationships, customer networks, transaction patterns, risk correlations, and market interconnections. Graph Large Language Models (Graph LLMs) represent the convergence of two powerful technologies: Graph Neural Networks (GNNs) that excel at modelling relationships, and Large Language Models that understand natural language and context.

The Core Architecture

Graph LLMs integrate Graph Neural Networks with Large Language Models to process both textual and relational data simultaneously. Think of it as giving traditional AI systems the ability to "see" not just individual data points, but the entire web of relationships connecting them. In banking terms, instead of analysing a customer's transaction in isolation, Graph LLMs understand the customer's position within their social network, transaction history, device usage patterns, and behavioural connections.

The difference between traditional AI and Graph LLMs is like the difference between reading individual words versus understanding the story they tell together.

Why Graph LLMs Outperform Traditional Models

The financial services industry generates data that is inherently relational. Customer behaviour, transaction flows, risk patterns, and market dynamics all exist within complex networks. Graph LLMs excel in six critical areas that make them superior to traditional approaches:

1) Relational Data Handling: Model complex relationships between customers, transactions, accounts, and policies

2) Contextual Nuance: Understand context at granular levels, improving insight accuracy

3) Business Intelligence: Map relationships to provide actionable intelligence for specific operational units

4) Deep Search Efficiency: Execute complex queries across relationships faster than traditional databases

5) Schema Flexibility: Adapt to changing business needs without rigid database constraints

6) Intuitive Modelling: Represent real-world scenarios in ways business users can understand and work with

Core Banking: Revolutionizing Customer Intelligence and Risk Management

Hyper-Personalized Customer Experience

Indian banks are already leveraging AI for customer service - HDFC Bank's chatbot Eva has answered over five million user queries, while SBI's Intelligent Assistant handles nearly 10,000 enquiries per second. Graph LLMs take this further by enabling true hyper-personalization through network analysisReal-World Impact: BNP Paribas Personal Finance implemented a graph-powered fraud detection system that reduced fraud losses by 20% while improving customer trust and regulatory compliance. The system analyses not just individual transactions but entire relationship networks to identify suspicious patterns.

Advanced Fraud Detection Networks

Traditional fraud detection in Indian banking focuses on individual transaction anomalies. Graph LLMs detect sophisticated fraud rings by mapping relationships between accounts, devices, IP addresses, and transaction patterns. Modern fraudsters don't work alone, they work in networks. To catch them, you need to think in networks too.

A graph-based fraud detection system can reduce false positive rates by 60-80%, allowing analysts to focus on real threats while detecting fraud 3-5 times faster. For Indian banks dealing with increasing UPI and digital payment fraud, this translates to potential savings of ¹25-100 crore annually in fraud detection and ¹10-50 crore in avoiding regulatory penalties.

Strategic Action Points for Core Banking

Insurance: Transforming Claims Processing and Risk Assessment

Automated Claims Revolution

The Indian insurance sector, projected to reach ¹387.95 million in generative AI investment by FY2032, faces significant efficiency challenges in claims processing. Traditional methods are labour-intensive and prone to fraud. Zurich Insurance's implementation of knowledge graphs enhanced claims processing efficiency while improving fraud detection and customer experience.

Graph LLMs revolutionize claims processing by analysing relationships between policyholders, past claims, incident reports, and external data sources. In insurance, every claim tells a story - Graph LLMs help you read between the lines.

Network-Based Risk Assessment

Tata AIA Life's AI-powered chatbot Tasha has handled 7.5 million customer interactions with a 98% completion rate. Graph LLMs extend this capability by incorporating social and behavioural networks into risk assessment, enabling more accurate underwriting for previously underserved populations.

Strategic Action Points for Insurance

Lending: Redefining Credit Assessment Through Network Intelligence

Alternative Credit Scoring

India's digital lending market is projected to reach $515 billion by 2030. Traditional credit scoring fails for the underbanked population, but Graph LLMs can analyse alternative data including social media activity, payment networks, and peer relationships to provide holistic creditworthiness assessment.

In lending, your network is often more predictive of your creditworthiness than your credit history.

Dynamic Risk Pricing

Graph LLMs enable dynamic pricing models that adjust in real-time based on interconnected factors including individual customer data, social influences, market trends, and behavioural patterns. This leads to more accurate, competitive, and personalized lending products.

Real-World Application: European banks using graph neural networks for client linking have demonstrated significant improvements in ROC AUC scores for link prediction problems while enhancing credit scoring quality.

These models use both topological network structure and rich time-series data available for graph nodes and edges.

Strategic Action Points for Lending

Investment Management: Network-Driven Portfolio Intelligence

Graph-Powered Portfolio Optimization

The investment management industry is experiencing significant AI adoption, with 91% of managers currently using or planning to use AI within their investment strategy or asset class research. Graph Neural Networks excel at portfolio optimization by modelling relationships between firms and capturing higher-order representations crucial for understanding financial market dynamics. In markets, everything is connected to everything else, the firms that map these connections best will generate the most alpha.

Network-Based Asset Analysis

Traditional portfolio construction approaches don't account for changing influences and relationships across assets. Graph-based AI systems start with asset return time series, learn structures across assets, and construct portfolios with structural intelligence about underlying dynamics, leading to robust outperformance with low drawdowns.

Indian Market Applications

Indian asset management companies are increasingly adopting AI for portfolio management. Quant Mutual Fund uses AI-powered algorithms to identify stock trends and rebalance portfolios dynamically. The rise of robo-advisory platforms in India, powered by algorithmic precision and risk management capabilities, demonstrates the growing acceptance of AI-driven investment strategies.

Strategic Action Points for Investment Management

The question isn't whether Graph LLMs will transform BFSI, it's whether you'll lead that transformation or be disrupted by it. The technology exists, the use cases are proven, and the ROI is demonstrable. The only question remaining is: Will you be among the industry leaders who harness the transformative potential of Graph LLMs, or will you watch from the sidelines as others reshape the future of finance? Financial institutions that embrace this technology now will establish competitive advantages that compound over time.

Your competitive advantage depends on how quickly you can turn your data relationships into business intelligence, your customer networks into personalized experiences, and your risk patterns into predictive insights.