External Balance Transfer Prediction Model Using Tradeline Data
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Solution Overview
Problem
Existing credit card portfolio management systems struggle to identify customers likely to respond to Balance Transfer (BT) offers from competitors, as external BT response information is not available due to lack of shared industry data.
Innovation Solution
A system and method using tradeline data to develop a pattern recognition model that predicts the likelihood of responding to external BT offers by analyzing utilization and balance patterns, and applying this model to external tradeline information to infer response probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external BT response information is not shared by competitors, then data privacy and security are maintained, but the ability to identify customers likely to respond to external BT offers is lost
Solution Approach 1:
The patent uses credit bureau tradeline data as an intermediary to indirectly infer external BT response information without requiring direct sharing of sensitive customer data between competitors. The system processes publicly available tradeline information to derive BT response probabilities, maintaining data privacy while enabling competitive intelligence.
Solution Approach 2:
The system creates a proxy model that copies the essential patterns of external BT response behavior from tradeline data, rather than requiring access to actual competitor response data. This allows the host entity to simulate and predict external BT responses using available public information.
2Loss of information
If traditional BT modeling uses only internal BT response data, then data availability is maintained, but the model cannot identify customers likely to respond to external BT offers
Solution Approach 1:
The patent transitions from analyzing only internal BT response data to incorporating external dimension data through tradeline information. By adding this new dimension of public credit bureau data, the model gains the capability to predict external BT responses while retaining all internal data advantages.
Solution Approach 2:
The system creates a multi-functional modeling approach that simultaneously leverages internal BT response data and external tradeline data to produce comprehensive BT response predictions. This universal model can identify customers likely to respond to both internal and external BT offers.
3Measurement precision
If a pattern recognition model is developed using tradeline level data, then external BT response prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex tradeline data into specific relevant features and patterns that are most indicative of external BT response behavior. By focusing on key segments of data rather than processing all raw information, the system achieves high prediction accuracy while managing processing complexity.
Data Source
AI summary
A system and method for determining a likelihood of response to a Balance Transfer (“BT”) offer includes developing a pattern recognition model based on BT response information contained in tradeline level data that has external tradeline information of a plurality of customers of a host financial institution, applying the pattern recognition model to the external tradeline information of the customers to determine a probability of whether the external tradeline information indicates that an BT offer was accepted by a customer, developing an overall customer account level model based on desired historical account behavior, applying the account level model to historical account behavior information of the customers to determine a likelihood of whether each customer will accept a BT offer from an external financial institution, and ranking the customers based on the determined probability and the likelihood that each customer would accept a BT offer from an external financial institution.