Competition in retail banking may be more intense than ever as FinTechs and new market entrants fight with established players for deposits and market share. Retail banks that embrace advanced analytics and leverage their valuable data can gain a decisive competitive advantage.
Predictive modeling is nothing new to the retail banking space. Nearly all retail banks have some form of predictive modeling built into their systems, and credit scoring has been around since the 1950s. Over the past decade especially, retail banks have led the way in analytics by curating consumer and transactional data and investing in data analytics, data science tools, and data platforms.
But recent advances in automated machine learning make it possible to put that data to use in dozens of new ways that were entirely infeasible or simply cost prohibitive just a few years ago. Banks can now build and deploy AI solutions not just for hyper accurate and granular credit risk measurement but also to acquire new clients, improve marketing effectiveness, predict client needs and deepen client relationships, identify attrition risks and causes, and fight financial crime.
1. Predicting client needs
Deeper client relationships are both more profitable and more loyal. By learning from their data, banks can identify, even anticipate, client needs that they can help with. Clients are far more likely to respond to a relevant offer and to have a favorable impression of your bank than they are if you are still sending indiscriminate offers with minuscule response rates.
2. Keeping existing customers is at least as important as finding new ones.
Banks can learn from their client interaction data to identify customers at risk of attrition and take preemptive action. Even better, good models can identify the leading causes of attrition risk so that you can make process adjustments or improvements in order to hold onto more of your most valuable clients.
3. Price optimization and lifetime value
Many banks use a score-carding process in consumer lending, determining what terms to offer if the borrower meets certain criteria. Often, these are based on risk appetite rather than any insight into price elasticity or profit margin/volume tradeoffs. If banks knew which clients were likely to be the most profitable and knew how those clients were likely to respond to price differences, then they might price more aggressively in order to land those clients.
With automated machine learning, you can easily and quickly build hundreds of models, learning as you go to understand how client behavior will differ across regions, channels, products, client segments, or risk spectrum, and understand in advance the effect of different pricing on volume, profit margin, and credit quality.
How to Learn More
Download the eBook 5 AI Solutions Every Retail Bank Needs for the full list of solutions.
To learn more about how your organization can capitalize on AI and machine learning, join DataRobot’s GM of Banking at our webinar on Wednesday, February 13th at 1:00 pm ET.
About the Author:
H.P. Bunaes is the GM of Banking at DataRobot, helping banks leverage AI and machine learning for predictive analytics and data mining. H.P. has 35 years 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, including leading the design and development of the risk data and analytics platform used enterprise-wide for risk management. H.P. is a graduate of the Massachusetts Institute of Technology where he earned a Masters Degree in Management Information Systems, and of Trinity College where he earned a Bachelor of Science degree in Computer Science and Mechanical Engineering.