Personalized Banking Products Recommendation
Recommending the best product to banking customers based on transaction data
What to offer next and why?
One of the key challenges the bank faced in the past with best next-offer solutions was that the recommendations were typically the output of a blackbox model that could seem out of place and couldn't be explained. As a result, a banker on the phone or sitting next to the customer would have no context and would be unable to engage with the customer on how the offer is a good fit. Our goal was to provide a meaningful narrative in addition to the predictions.
Client-journey-driven segmentation
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Rich history from transaction data
Based on detailed transaction data from various sources, the algorithm automatically combines them in an intelligent way and segments customers into different client journey paths.
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Prediction of demand for each product
Given where the customer is at on their client journey, the model predicts the demand for various products, which serves as inputs to the recommendation engine.
Illustrative Optimal Trees for banking product recommendations
Recommendations that increased customer engagement
In a pilot study under the recommendations, the bank estimated a significant lift of customer retention rate and increased product uptake, especially in high-value products such as mortgage offerings.
Unique Advantage
Why is the Interpretable AI solution unique?
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Actionable with validated insights
The model outputs recommended products and rationale, providing the bankers with the context they need for better engagement
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Signals from complex data
Optimal Trees is able to sift through large volume and various sources of transaction data to learn what's most important in defining the client journey