In all segments of commercial banking competition is more intense and top line growth harder to achieve than ever before.
Leading banks are rapidly innovating with AI to find growth opportunities, increase efficiency, improve the delivery of services to clients, and increase the effectiveness and efficiency of regulatory compliance. First movers will realize sustainable competitive advantage and those who fall behind may not ever be able to close the gap. Commercial banks that learn from their data will find and capture the best new clients and deepen existing client relationships. And they will improve the client experience by anticipating what clients need and providing AI-based insights to help commercial clients manage their business.
In our eBook, 6 AI Solutions Every Commercial Bank Needs, we describe ways that AI can be leveraged right now, using data that most banks have readily available today.
Here’s a closer look at three examples:
Prospecting, Leads, Referrals
How do you know which leads and referrals are the most valuable? Is there a better and more efficient way to identify and pick out the right ones? Yes, there is. With automated machine learning, you can use the data about current clients to spot the prospects that are most likely to become the highly profitable relationships of tomorrow. Using historical data and AI you can identify the clients most likely to benefit from your products and services and then target them precisely.
Knowing what your clients need and when they need it is essential to any successful commercial bank. This insight can be achieved and leveraged with AI and machine learning in two ways. First, by learning from historical client data on what products and services clients have needed in the past you can predict what a client is likely to need today. Second, you can identify trigger events which indicate that a new need may have arisen. Knowing your clients and understanding and anticipating their needs will deepen relationships increasing both relationship profitability and client loyalty.
Treasury and Cash Management
Company treasurers often struggle to forecast cash flows and anticipate liquidity shortfalls or excesses. Banks can leverage AI and machine learning to predict client cash flows based on historical transactions (sources and uses of cash). Banks can then help commercial clients forecast account balances and warn them of potential shortfalls. Relationship Managers can proactively offer credit products to meet short term liquidity needs or cash management products to optimize returns on excess cash.
Ready to learn more? Download our eBook, 6 AI Solutions Every Commercial Bank Needs.
Get ahead and stay ahead of the competition with DataRobot.
About the Author:
H.P. Bunaes leads the banking industry vertical at DataRobot H.P. has over 30 years of experience in banking, with broad banking domain knowledge and deep expertise in data and analytics. Prior to joining DataRobot, H.P. held a variety of leadership positions at SunTrust and FleetBoston. H.P. is a graduate of Trinity College and earned his masters degree from the Massachusetts Institute of Technology.