The future of work for commercial banking RM's is set to be transformed by the integration of generative AI agents. As the financial services industry evolves, AI will become an essential tool, automating tasks and enhancing productivity. Here's how AI will revolutionize key activities for RM's:
Portfolio Management: AI agents will help relationship managers make data-driven decisions by analyzing vast amounts of financial data, identifying risks, and forecasting client needs. This will lead to better portfolio optimization and more personalized financial advice. In addition, aggregation of individual account plan insights will provide an overview of portfolio productivity and highlight potential white space.
Account Planning: AI can support strategic account planning by identifying growth opportunities and suggesting tailored solutions for clients. It can synthesize information from CRM systems, alternative data, and core banking systems, allowing managers to focus on relationship-building rather than time-consuming data gathering.
Business Development & Cross-Selling: AI agents can analyze customer behavior and transaction history to identify cross-selling and upselling opportunities. By generating real-time insights, AI will enable relationship managers to develop customized proposals quickly and effectively, improving client satisfaction and driving revenue growth. These agents will also have access to significant volumes of bank documentation, policies and product information to accurately convey key benefits and outcomes for each client.
Reducing Compliance Burden: Regulatory compliance can be time-consuming, but AI can automate much of the compliance-related administrative work. By interpreting regulations, monitoring transactions, and generating necessary documentation, AI will free up relationship managers to focus on high-value activities. In addition, these agents will be able to combine information from bank policy documentation with real-time data activity on customers to help bankers remain compliant at all times.
Streamlining Loan Applications: The traditionally slow process of loan application review and approval will be significantly expedited by AI agents. From summarising customer information and documentation, to auto-generating credit proposal content, AI will make the loan process smoother and faster for both clients and banks. Generative AI agents will also provide guidance to RMs and credit analysts on credit proposal quality, reducing re-work rates and improving overall credit quality.
A Day in the Life: Collaborating with Generative AI Agents
Imagine the typical workday of a commercial banking relationship manager (RM) a few years from now, collaborating seamlessly with generative AI agents:
8:00 AM - Morning Briefing: The RM begins the day with a personalized report generated by the AI agent. Overnight, the agent has analyzed key data points, including updates from client portfolios, financial statements, and market trends. The report highlights clients with potential risks and opportunities, making the RM's portfolio review more efficient. The AI agent ensures the most critical activities for the day are being presented to the RM for review and action.
9:00 AM - Account Planning Session: The RM prepares for a client meeting. With a few prompts, the AI agent compiles insights from CRM, financial history, and even alternative data sources like industry reports and external news. The agent suggests cross-selling opportunities, noting the client's increased cash flow and potential need for working capital solutions.
11:00 AM - Client Meeting: During a virtual meeting with a client, the RM collaborates with the AI agent in real-time. As the conversation unfolds, the AI suggests relevant financial solutions and adjusts the proposal based on the client's responses. The agent also automatically summarizes key points from the meeting and updates the CRM system.
1:00 PM - Compliance Check: The RM receives an alert from the AI agent indicating a large transaction that will overdraw the client's bank account. The agent has already analyzed the financial statements, transaction information and payment history and provided a recommendation on temporary line-of-credit amount and expected clearance. Throughout the process the AI agent cross-checks all activities against the bank's credit policy, finally the RM reviews and signs off, knowing that the compliance burden has been drastically reduced.
3:00 PM - Loan Application Processing: The RM receives a new loan application. Rather than manually reviewing the financials, the AI agent assesses the client's background, trends and performs financial analysis using historical data and financial ratios. It flags potential risks and generates a discussion Q&A list for the RM to discuss with the client.
4:00 PM - Business Development: The AI agent identifies two clients with evolving needs, based on recent transaction data and market insights. It generates a tailored outreach plan, complete with potential solutions to cross-sell. The RM uses the AI's insights to initiate meaningful conversations with the clients, focusing on relationship building and growth.
5:30 PM - End of Day Summary: As the day ends, the AI agent provides a summary of activities, updates the RM's pipeline, and suggests follow-ups for the next day. Tasks like scheduling, documentation, and analysis have been automated, allowing the RM to focus on high-impact activities throughout the day.
Generative AI agents will not only reduce the administrative burden on commercial banking relationship managers but also enhance their ability to deliver personalized, timely solutions to clients. From streamlining processes to providing real-time insights, AI will empower relationship managers to focus on building stronger client relationships and driving business growth. The future of commercial banking is bright for those who embrace the power of AI.
