Financial Product Sales Recommender System
An international, award-winning bank needed to recommend the right financial products to the right clients at scale. Praelexis built a machine learning recommender system to do it.
A Machine Learning Recommender System Built for Financial Services
The bank's customer base and product range had outgrown manual recommendation methods. Praelexis developed a matrix factorisation model that ranks financial products by relevance for each client, delivered through an API for easy integration into existing systems.
Manual product recommendations couldn't keep pace with a large customer base and a wide product range. Sales opportunities were missed and clients received offers that didn't match their needs.
Praelexis built a machine learning model to analyse customer behaviour and rank financial products by relevance. The project ran in two phases, scoring and implementation, so the bank could adopt the solution incrementally. An API made the system future-proof for integration across the bank's broader ecosystem.
Key Outcomes
Higher conversion
Increased engagement
Enhanced satisfaction
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