Predict the propensity of a customer for a specific behavior from historical transactional patterns. Traditional ML methods require significant hand crafted feature engineering.
- 1.Automated feature engineering for transactional financial data.
- 2.Automated explanations consumable by business owners.
- 3.Automated cluster naming that assists strategies.
- 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.
Significant lift in GINI score (10-25%) as compared to traditional models in current implementations.