Why AI Product Recommendations in Banking Have to Explain Themselves First
TL;DR Most conversations about AI in banking marketing use “next best offer,” “next best action,” and “product recommendation” as if they were interchangeable. They aren’t. A recommendation engine answers one question: which product should this customer see next. A next-best-action system answers a bigger one: whether to act at all, through which channel, at what […]
Why Customer Churn Prediction in Retail Banking Starts With the Customers Who Never Leave
TL;DR Ask a retail bank how many customers it lost last quarter, and most will pull up a report of closed accounts. That number is real. It is also increasingly beside the point. J.D. Power’s 2026 U.S. Retail Banking Satisfaction Study found that 20% of retail bank customers moved money away from their primary bank […]
Building a Churn Prediction Model Starts With the Label
TL;DR A propensity model’s build sequence applies to churn prediction the same way it applies to credit or growth models: source the data, engineer features, validate, then deploy. What changes for churn is the label itself, the features that actually carry signal, and the imbalance that sits underneath the data before a single model gets […]
Churn Prediction for Banks and Insurers Needs More Than a SaaS Retention Tool
TL;DR A churn model that only tells a relationship manager a customer might leave is describing something the relationship manager may already sense. The value sits in catching it early enough, and specifically enough, that someone can act before the account is already gone in spirit, if not yet on paper. Using a churn prediction […]
The Science of the Perfect Offer: Uniting Sentiment Analysis, Prediction, and Price Elasticity
For decades, banks have defaulted to “blanket offers”, broad rate increases and generic promotions to drive deposit growth. While this shotgun approach may appear to generate new activity, it too often results in high acquisition costs for money that is simply shifted internally rather than bringing in true net new money (NNM). In an era […]
5 Proven AI Strategies to Stop Customer Attrition in Banking Before It Happens
What if you could predict when a customer is about to leave before they even realize it themselves? In today’s fiercely competitive banking landscape, financial institutions spend millions acquiring new customers. Yet, the quiet leak in their deposit base through customer attrition continues to drain revenue faster than new acquisition can replenish it. It’s not […]
Churn Prediction for South African Banks and Insurers: Using AI to Prevent Customer Loss Before It Happens
TL;DR Acquiring a new retail banking customer in South Africa costs between R800 and R2,500 depending on the acquisition channel, product type, and customer segment. Retaining an existing one costs a fraction of that figure, provided the institution knows the customer is at risk before they leave. Churn prediction is the capability that creates that […]
