Reduce credit card customer churn using advanced machine-learning models

Attrition for credit cards is more complex than other products as customers with low/no fee products choose to silently attrite with no engagement and customers with fee products mostly choose to close the card. In either case, revenue is lost specifically if attrition is happening more from revolvers.

Typically, organizations react to attrition by making offers after customers have already decided to leave. This reactive approach often results in higher costs to counter competitive offers or failure to re-engage silent attritors. The solution lies in predicting attrition ahead of time to counter it with offers before the customers actually disengage.

This solution promotes a proactive strategy by introducing lead time to respond to attrition. This enables organizations to reach out to customers well in advance of their decision to leave, allowing for effective course corrections in the customer engagement lifecycle.

This solution leverages an advanced Machine Learning model to predict customer attrition probability well before it occurs.

The model detects behavioral changes that signal impending attrition and ranks these customers higher in priority. This can be used to track both silent attritors and card closures.

By integrating financial behavior with customer contact information and digital navigation patterns, the model achieves greater predictive accuracy.

The bank can evaluate the risk every quarter and can have 45-90 days of lead time before the customer shows early signs of attrition.

This solution is designed for businesses that:

1. Experience high attrition and wish to uncover the causes.
2. Intend to implement a proactive strategy to reduce customer attrition.
3. Have specific revenue targets to achieve by systematically reducing attrition.

Revenue: Achieve increased long-term revenue through higher customer engagement over a 2-5 year period.

Cost: Expect lower attrition and, consequently, reduced acquisition costs. This solution is designed to enhance customer loyalty over time.

The model incorporates the following characteristics:

1. Category-specific spending data
2. Credit limit utilization
3. Attrition data
4. Payment behavior patterns
5. Risk profiling
6. Contact information

The following models can be employed:

1. Classical logistic regression models
2. Gradient Boosting
3. Extreme Gradient Boosting (XGBoost)
4. Support Vector Machine (SVM)
5. Random Forest
6. Ensemble models

The best strategy is to test various models and choose the one with the highest performance.

1. On-prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

The final outputs may consist of customer-level attrition probabilities.

Batch files, including Excel files or flat files.
Outputs can be delivered via API requests. Send an API request with inputs and receive a response. This can be integrated into an existing application or a separate web application can be developed.