How AI Helps Banks Identify Cross-Selling and Upselling Opportunities
Banks regularly create campaigns to offer new products to their customers. According to market research, it’s easier to sell a product to an existing customer than to sell a product to a new, qualified prospect. In today’s marketplace, banks provide a variety of deposit, lending, and investment products to individuals and businesses, but a good proportion of current customers might only use one or two of them. Because customers can pick the most affordable product from one bank and hunt for other products elsewhere, it can be challenging to sell a full range of financial products to a single customer. Fortunately, banks have a valuable asset: customer data. With the right approach, banks can evaluate their customers’ data and generate insights on cross-selling and upselling opportunities. They can also use that data to provide more personalized products and financial advice. This is where artificial intelligence (AI) comes in.
Many banks use DataRobot’s automated machine learning (AutoML) to interpret customer data. By identifying people who are open to cross-selling, banks are in a better position to increase their revenues.
Banks are applying automation in their machine learning workflows for the following reasons:
- Machine learning allows banks to evaluate buyer behavior at the account level through an analysis of the most recent activities. Instead of push-based selling or one-size-fits-all campaigns, banks can personalize their products to a targeted group, which can improve a campaign’s return on investment.
- Machine learning helps banks provide a better customer experience by anticipating a customer’s needs. For example, a customer who bought tickets to an overseas destination might receive a push notification on their mobile phone when they arrive at the airport for the best foreign exchange deals. Or a customer who has encountered an unexpected expense might be offered an emergency loan at a competitive rate.
AutoML helps surface actionable insights that can be used by relationship managers to engage with customers more intelligently and more often.
In machine learning models, there’s an assumption that the future will always mirror the past, but customer behaviors do change over the time. A model can become invalid if the behavioral assumptions that were made when the model was trained are no longer accurate. In addition, model variables might change with market trends or time. Banks must keep a close eye on performance and data drift to avoid risks to their business.
DataRobot machine learning operations (MLOps) brings together data scientists and operations professionals to better manage the machine learning lifecycle. It provides monitoring capabilities that are not limited to performance, but also take into account the principle of humility in AI, so that data scientists can identify when models are losing accuracy or when they’re not confident in their predictions.
Machine learning isn’t a silver bullet for achieving cross-selling and upselling in banking. It can’t generate trustworthy insights for all customer behavior. That’s because customer behavior is affected by everything from the weather to the country’s politics. However, machine learning offers a much better solution for insightfully allocating marketing dollars than running financial marketing campaigns on underdeveloped research and half-baked ideas.
Lead Data Scientist at DataRobot
Javier Lombana is a Lead Customer-Facing Data Scientist at DataRobot working in the United Kingdom, and leading DataRobot’s banking data science practice supporting customers across multiple EMEA regions. Javier has more than 15 years of experience in Data Science and Analytics and before joining DataRobot he worked as Data Science Manager at Capgemini Financial Services. He owns a M.sC in Data Science from Universitat Oberta de Catalunya and a B.sC in Computers Science Engineering from the Polytechnic University of Madrid.
Customer Facing Data Scientist at DataRobot
Dr. Qian Zhao is a London-based data scientist at DataRobot who helps fintech, banking, and healthcare customers accelerate their machine learning capabilities by using the DataRobot platform. Before he joined DataRobot, Qian was a data science manager at PwC and Deloitte, where he led a team of data scientists, delivering innovative and cutting-edge AI and machine learning solutions to enterprise businesses. Qian received his Ph.D. in Neuroscience from UCL in 2012.
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