Banking Product Recommendation Engine

A multinational investment bank and financial services company headquartered in New York City, operates through multiple subsidiaries in more than 17 countries with more than 10,000 employees. It operates in global markets with capital origination, corporate and investment banking, markets and securities services, private bank, and treasury and trade solutions, as well as in retail banking.


Problem Statement

The company faced challenges in providing personalized banking product recommendations to its customers, leading to low conversion rates and high customer churn.

Our Solution

We developed a banking product recommendation engine using Gen AI-powered recommendation algorithms and machine learning models to analyze customer data and preferences. This solution consisted of:

  • Gen AI-powered Recommendation Algorithms: We Analyzed customer data and preferences to identify patterns and trends.
  • Generated personalized product recommendations based on customer interests and behaviors.
  • Machine Learning Models: We Used machine learning models to predict customer behavior and preferences.
  • Adapted product recommendations based on customer interactions and feedback.
  • Product Recommendation: We Created personalized product recommendations for each customer based on their interests and preferences.
  • Used various channels such as online banking, mobile apps, and branch visits to deliver personalized product recommendations.

Business Benefits

  • Improved Customer Engagement: The banking product recommendation engine improved customer engagement by 25% through personalized and relevant product recommendations.
  • Increased Conversion Rates: The solution increased conversion rates by 8% through targeted and personalized product recommendations.
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