Reward Liability Prediction Model for Program Sustainability
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Solution Overview
Problem
Current reward programs lack effective tools for predicting reward liability, leading to increased financial burdens for issuers due to unmanaged fund consumption and potential fraud, without considering future fund liability data, resulting in unsustainable programs.
Innovation Solution
A computer-implemented method and system that accesses historical reward data to determine seasonality patterns and trains a reward liability prediction model using a SARIMA time-series model with exogenous variables, enabling accurate forecasting of future reward liabilities and dynamic adjustment of reward program rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reward programs offer generous incentives to strengthen customer relationships, then customer loyalty and engagement improve, but fund liability and financial burden on issuers increase
Solution Approach 1:
The system performs preliminary actions by training a reward liability prediction model using historical reward data and seasonality patterns before executing reward programs. This allows issuers to forecast future reward liabilities and adjust incentive levels in advance, preventing excessive fund consumption while maintaining customer loyalty.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring reward program performance and using prediction models to provide real-time insights on fund liability trends. This feedback loop enables dynamic adjustment of reward incentives to balance customer engagement with financial sustainability.
2Ease of operation
If reward programs run without considering future fund liability data, then program flexibility and customer satisfaction improve, but fund consumption increases and program sustainability deteriorates
Solution Approach 1:
The system performs preliminary analysis by training prediction models on historical data before reward programs execute, enabling issuers to set sustainable reward structures in advance without restricting operational flexibility during program execution.
Solution Approach 2:
The system changes parameters by dynamically adjusting reward incentive levels based on prediction model outputs. This allows the program to maintain flexibility in responding to customer behavior while ensuring long-term sustainability through data-driven parameter optimization.
3Device complexity
If traditional reward programs operate without prediction tools, then implementation simplicity is maintained, but ability to manage and minimize potential losses deteriorates
Solution Approach 1:
The system introduces an intermediary prediction model that sits between historical data and reward program decisions. This intermediary layer provides loss management capabilities without significantly complicating the overall system, as the model operates autonomously to generate predictions that guide reward allocations.
4Reliability
If reward programs increase incentive levels to maximize loyalty effect, then customer engagement improves, but reward liability and potential losses increase
Solution Approach 1:
The system applies preliminary anti-action by using prediction models to identify and prevent excessive reward liability before it occurs. The model forecasts potential losses at different incentive levels, allowing issuers to set incentive thresholds that prevent harmful liability accumulation while maintaining effective customer engagement.
Data Source
AI summary
Methods and systems are provided for predicting reward liability data of reward programs. A method includes accessing, by a server system, historical reward related data associated with one or more reward programs administered by a reward program provider of reward program providers. The historical reward related data includes past redeemed reward points for each reward program aggregated on a particular time basis. Method includes identifying first seasonality patterns and second seasonality patterns associated with the historical reward related data. Method includes training a reward liability prediction model based on first and second seasonality patterns, wherein the trained time-series prediction model is configured to predict future reward liability data associated with the one or more reward programs. Upon training the model, the method includes modifying reward rules associated with the one or more reward programs based on predicted future reward liability data and one or more reward liability criteria.