The future of commercial bank RM work is poised 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 improving productivity. Here’s how AI will revolutionize key RM activities:

Portfolio management: AI agents will help relationship managers make data-driven decisions by analyzing large amounts of financial data, identifying risks and predicting customer needs. This will lead to better portfolio optimization and more personalized financial advice. Additionally, aggregating information across individual account plans will provide insight into portfolio productivity and highlight potential white spaces.

Account planning: AI can support strategic account planning by identifying growth opportunities and suggesting tailored solutions to clients. It can synthesize information from CRM systems, alternative data and core banking systems, allowing managers to focus on building relationships rather than tedious data collection.

Business development and cross-selling: AI agents can analyze customer behavior and transaction history to identify cross-sell and upsell opportunities. By generating real-time insights, AI will enable account managers to develop personalized propositions quickly and efficiently, improving customer satisfaction and driving revenue growth. These agents will also have access to large volumes of banking documents, policies and product information to accurately convey key benefits and outcomes for each customer.

Reduce the compliance burden: Regulatory compliance can be time-consuming, but AI can automate much of the administrative work related to compliance. By interpreting regulations, monitoring transactions and generating the necessary documentation, AI will allow relationship managers to focus on high value-added activities. Additionally, these agents will be able to combine information from bank policy documents with real-time customer activity data to help bankers stay compliant at all times.

Streamlining loan applications: The traditionally slow process of reviewing and approving loan applications will be significantly accelerated by AI agents. From summarizing customer information and documentation to automatically generating credit proposal content, AI will make the lending process smoother and faster for both customers and banks. Generative AI agents will also provide advice to RMs and credit analysts on the quality of credit proposals, thereby reducing rework 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 a.m. – Morning briefing: The RM starts the day with a personalized report generated by the AI ​​agent. Overnight, the agent analyzed key data points including client portfolio updates, financial statements and market trends. The report highlights clients with potential risks and opportunities, making the RM’s portfolio review more effective. The AI ​​agent ensures that the most critical activities of the day are presented to the RM for review and action.

9:00 a.m. – Account planning session: The RM is preparing for a client meeting. With just a few prompts, the AI ​​agent compiles information from CRM, financial history, and even alternative data sources such as 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 a.m. – Customer meeting: During a virtual meeting with a client, the RM collaborates with the AI ​​agent in real time. As the conversation progresses, the AI ​​suggests relevant financial solutions and adjusts the proposal based on the customer’s responses. The agent also automatically summarizes key points from the meeting and updates the CRM system.

1:00 p.m. – Compliance check: The RM receives an alert from the AI ​​agent indicating a large transaction that will overdraw the customer’s bank account. The agent has already analyzed the financial statements, transaction information and payment history and provided a recommendation on the temporary credit line amount and anticipated authorization. Throughout the process, the AI ​​agent checks all activities against the bank’s credit policy, then the RM reviews and approves them, knowing that the compliance burden has been significantly reduced.

3:00 p.m. – Processing loan applications: The RM receives a new loan request. Rather than manually reviewing financial data, the AI ​​agent assesses the customer’s history and trends and performs financial analysis using historical data and financial ratios. It flags potential risks and generates a list of questions and answers for the RM to discuss with the client.

4:00 p.m. – Business development: The AI ​​agent identifies two customers whose needs are changing, based on recent transaction data and market information. It generates a tailored outreach plan, complete with potential cross-selling solutions. RM uses AI insights to engage in meaningful conversations with customers, focusing on relationship building and growth.

5:30 p.m. – End of day summary: At the end of the day, the AI ​​agent provides a summary of activities, updates the RM pipeline, and suggests follow-ups for the next day. Tasks such as planning, 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 improve their ability to provide personalized and timely solutions to customers. From streamlining processes to providing real-time insights, AI will enable relationship managers to focus on building stronger customer relationships and driving business growth. The future of commercial banking is bright for those who embrace the power of AI.