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AI in Corporate Banking: Transforming Relationship Management
AI-powered insights are transforming corporate banking by enhancing relationship managers’ efficiency.
Corporate banking has long been relationship-driven, relying on relationship managers (RMs) to understand client needs, identify opportunities, and provide tailored financial solutions. However, RMs today face increasing complexity - vast amounts of client data, evolving market conditions, and increasing expectations for seamless, personalized service. In fact, 66% of B2B customers expect fully or mostly personalized content when buying the products and services, according to a Forrester study for Adobe.
Despite the wealth of available data, most RMs still spend significant time manually gathering and interpreting information - whether preparing for client meetings, searching for cross-sell opportunities, or responding to client inquiries. This inefficiency limits their ability to focus on high-value interactions and strategic decision-making.
AI is becoming a game-changer in corporate banking sales. According to LXA, 64% of B2B marketers consider AI valuable for their strategy. Large Language Models (LLMs) like ChatGPT, Claude, and Gemini have revolutionized AI-driven engagement. In fact, McKinsey identified sales and marketing as one of the areas where generative AI and large learning models (LLMs) can deliver the most value. AI is playing a critical role in streamlining workflows, enhancing decision-making, and ultimately improving client engagement.
Our previous blog, Knowledge Graphs and LLMs: A Practical Guide to the Next Frontier for B2B Sales, talked about the underlying AI technology for B2B sales. This post will explore the practical scenarios where AI enhances RM’s workflow in corporate banking sales.
How AI enhances the Relationship Manager experience
AI isn't new, but GenAI is. GenAI fundamentally changes how RMs interact with data and clients. With AI-driven insights powered by Knowledge Graphs and Retrieval-Augmented Generation (RAG), RMs can move from manual information gathering to proactive, insight-driven engagement.
Practical scenarios: AI in action
Preparing for a client meeting
Before meeting a corporate client, an RM can receive a concise, AI-generated summary of the client’s latest transactions, news mentions, industry trends, and relationship history - all in seconds, rather than hours of manual research.
AI can surface hidden opportunities, such as a shift in working capital needs or an upcoming refinancing event based on recent financials.
Example questions RMs might ask AI:
“Give me all the news relevant to this client this year?”
“Has there been any significant transaction activity in the past quarter?”
“Summarize recent call notes with this client”
Identifying new opportunities for a specific product
Suppose an RM wants to find companies that may benefit from a supply chain financing solution. AI can analyze client portfolios, recent transaction patterns, cash flow trends, and external market signals to suggest a prioritized list of prospects who are most likely to need this product.
Example questions RMs might ask AI:
“Which of my clients have increased their international trade activity in the past six months?”
“Are there clients showing signs of cash flow constraints that might benefit from working capital solutions?”
“Which of my clients have pending debt maturities that might require refinancing?”
Answering client questions in real time
When a client asks, “How does my company’s liquidity position compare to similar firms in my industry?” Instead of manually searching through reports or seeking help from busy data scientists, the RM can use AI to quickly retrieve relevant benchmarking insights - backed by real-time financial data and market trends - rather than manually searching through reports or making requests with long lead times to data science or analytics teams.
Tracking industry trends and client-specific risks
AI can continuously monitor and flag industry shifts, regulatory changes, or macroeconomic factors that could impact key clients.
Proactive alerts enable RMs to reach out with relevant insights before clients even ask, positioning them as trusted advisors.
Example questions RMs might ask AI:
“Which of my clients are most exposed to recent commodity price fluctuations?”
“Which are the tier 2 suppliers for this client?”
“What is the risk (internal/external) associated with suppliers for this client?”
How AI powers these scenarios
AI-driven insights are only as good as the data that fuels them. This is where Knowledge Graphs and Retrieval-Augmented Generation (RAG) play a critical role:
Knowledge Graphs represent a holistic client network view from connecting internal data (such as CRM, transaction history, and risk assessments) with external data (such as corporate registries, market news, industry reports, and regulatory updates)
Retrieval-Augmented Generation (RAG) solves the LLM data gap. Traditional LLMs rely mainly on public datasets and cannot access private, enterprise-specific data. RAG allows LLMs to securely retrieve real-time, enterprise-specific insights before generating responses, ensuring AI-driven insights are accurate, up-to-date, and relevant.
Dynamic context awareness enables AI-driven insights that are deeply personalized to each client. By combining AI models with real-time data retrieval, RMs get precise, situation-aware recommendations rather than generic AI-generated content. This combination ensures AI-driven insights are not just broad industry trends but deeply personalized to each client, their history, and their unique financial needs. As AI evolves with Generative AI and advanced retrieval mechanisms, it becomes more capable of capturing dynamic, real-world changes in client interactions.
AI in corporate banking in action
Here is a real world scenario of how corporate banking relationships managers can be enabled with a conversational interface to access a contextual view of clients and prospects.
Background and solution:
A global tier 1 bank aimed to increase productivity and enhance corporate banking sales and servicing by leveraging AI technologies. By connecting internal data such as customers, hierarchies, payments, trades, contacts, and engagement, with external data like corporate registries, market events, news, and ESG, Quantexa created a knowledge graph mapping relationships between businesses, corporate hierarchies, people, places, supply chains, and market events. This knowledge graph served as the contextual foundation to augment Open AI LLMs.
Outcome:
Relationship Managers have access to a conversational interface embedded in their CRM system (e.g., Microsoft Dynamics), providing comprehensive, real-time, and accurate responses to questions such as:
“Which of my clients has the greatest exposure to electric vehicle manufacturing companies in China?”
“Who are Quantexa's key suppliers? Which are already clients of the bank? Which are attractive prospects?”
“Provide a summary of Apple's key buyers and suppliers and any recent news”
“Which clients have established new overseas subsidiaries in the last 3 months? Which are not existing customers?”
Key considerations for adoption
While the benefits are clear, adopting AI in corporate banking requires thoughtful planning. Key considerations include:
Model governance & compliance: AI must meet regulatory requirements, ensure data security, and comply with industry standards for financial services. This can be particularly complex with global deployments that must comply with a varied and quickly evolving regulatory landscape.
Bridging the private data gap: Standard LLMs don’t use private data, limiting their ability to generate enterprise-specific insights. RAG bridges this gap by allowing AI to securely pull in proprietary data without needing to retrain the underlying models.
Considering prompt engineering and model strategies:
While RAG enables AI to access enterprise data, prompt engineering can further enhance AI’s ability to generate domain-specific terminology, financial regulations, and client interaction nuances.
Banks need to determine the right approach–whether to rely entirely on RAG-based retrieval, apply prompt engineering for more precise response, or in some cases, explore model fine-tuning for highly specialized needs.
Continuous monitoring and adaptive learning strategies should be in place to improve model accuracy and ensure alignment with evolving business needs.
Change management and adoption: AI should be integrated into existing RM workflows and tools (e.g., CRM platforms) to drive seamless adoption without disrupting RM’s day-to-day processes.
The path forward
For banks looking to embed AI into corporate banking, the best approach is to start small and scale strategically:
Identify quick win use cases
Start with areas where AI can deliver immediate value, such as client meeting prep and opportunity identification.
Pilot AI-powered insights with a small group of RMs before broader rollout.
Leverage existing data assets
Ensure structured and unstructured data sources are connected and optimized for AI retrieval.
Build a Knowledge Graph that integrates internal and external data sources.
Pilot & iterate for quick returns
A well-structured AI pilot can deliver measurable improvements in RM productivity within 3-6 months.
Focus on KPIs like time saved in client research, increase in client engagement, and conversion rates from AI-driven insights.
Scale with confidence
Once early use cases prove successful, expand AI-driven insights across product lines and geographies.
Invest in ongoing AI model optimization and governance to maximize ROI.
Take the first step today
The shift toward AI-driven relationship management is no longer optional—those that don’t act now risk being left behind. Banks that embrace Knowledge Graphs and RAG-powered AI today will be better positioned to deliver hyper-personalized client engagement, uncover new opportunities, and drive revenue growth.
Want to see it in action? Request a demo of Quantexa's Decision Intelligence Platform or speak with our experts to explore how AI-powered insights can transform your RM experience.
