Benefit Surrender Prediction Using Survival Analysis
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Solution Overview
Problem
Current systems for predicting client benefit surrender lack the ability to identify a variable time scale for surrender and are inefficient in handling larger datasets, leading to ineffective preventive measures and significant revenue impact on organizations.
Innovation Solution
A deep learning-driven benefit surrender prediction system that includes a processor, data extractor, data analyzer, and modeler to create a neural network-based model, which determines survival and hazard probabilities to predict the time of benefit surrender, optimizing computational resources and handling large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-based learning models are used to predict clients likely to surrender benefits, then prediction capability is improved, but the ability to identify associated time scale of surrender is lost
Solution Approach 1:
The patent segments the prediction task into two distinct components: (1) identifying clients likely to surrender benefits using AI-based learning models, and (2) determining the time scale of surrender using survival analysis. This segmentation allows each component to specialize in its strength while collectively providing comprehensive predictions including both client identification and timing information.
Solution Approach 2:
The patent merges two analytical approaches - AI-based learning models for client identification and survival analysis for time scale determination - into a unified prediction framework. This combination enables the system to simultaneously provide both the 'who' (client identification) and 'when' (time scale) aspects of benefit surrender prediction.
2Ease of manufacture
If traditional predictive approaches are used for client retention, then implementation is simple, but computation time increases significantly for larger datasets
Solution Approach 1:
The patent replaces traditional mechanical computing approaches with optimized algorithms that leverage the specific mathematical properties of survival analysis. This substitution enables efficient processing of large datasets by utilizing specialized computational methods rather than general-purpose algorithms, significantly reducing computation time while maintaining implementation feasibility.
3Productivity
If preventive measures are launched without knowledge of surrender time scale, then client retention operations can be initiated, but effectiveness of retention strategies decreases
Solution Approach 1:
The patent enables preliminary identification of both the clients likely to surrender and the time scale of surrender before retention operations are launched. This preliminary knowledge allows organizations to timing their retention strategies optimally, launching preventive measures at the most effective moment rather than arbitrarily, thereby significantly improving retention strategy effectiveness.
Data Source
AI summary
A benefit prediction requirement may be received and associated benefit data from a plurality of sources may be obtained. The benefit prediction requirement may be associated with determining a probability of a benefit being surrendered by a benefit user. Further, a plurality of benefit attributes may be identified and mapped with benefit user data to create a benefit surrender database. From the benefit surrender database, a survival probability and a hazard probability may be determined and benefit assessment data may be created therefrom. Based on the benefit assessment data, a benefit surrender prediction model including a surrender probability may be created and a surrender pattern of the benefit user may be determined. Furthermore, a benefit surrender result may be generated and a remedial action in response to the benefit prediction requirement may be performed.