LOADING...

SUCCESS STORIES

PROPENSITY

Problem/Challenges

Predict the propensity of a customer for a specific behavior from historical transactional patterns. Traditional ML methods require significant hand crafted feature engineering.

Innovation

  • 1.Automated feature engineering for transactional financial data.
  • 2.Automated explanations consumable by business owners.
  • 3.Automated cluster naming that assists strategies.

Approach

  • 1.The challenge here is to use historical transactional data and patterns to understand the customer's propensity to buy a new product.
  • 2.Traditional approach, involved segmenting customers based on demographics.
  • 3.A multi layer deep learning network was constructed to analyze the financial transactions and identify the behavioral patterns.
  • 4.The behavioral patterns were then segmented using a clustering techniques, and the clusters were auto named.
  • 5.Based on the behavior, the propensity of the customer to buy financial products was estimated.

Result

Significant lift in GINI score (10-25%) as compared to traditional models in current implementations.