Gradient-Boosted Decision Tree for Financial Event Prediction
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Solution Overview
Problem
Financial institutions face challenges in predicting customers' propensity to acquire additional financial products, such as secondary checking accounts, due to limitations in detecting subtle changes in saving, spending, and interaction habits, and existing adaptive techniques fail to characterize the likelihood of acquiring secondary accounts from primary account holders.
Innovation Solution
The implementation of a machine-learning or artificial-intelligence process, specifically a gradient-boosted decision-tree process, is trained using customer-specific datasets to predict the likelihood of targeted acquisition events, including the acquisition of secondary checking accounts, by analyzing customer interaction data across different temporal intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used to predict customer acquisition behavior, then the system is simple to implement, but the prediction accuracy is insufficient due to inability to detect subtle changes in customer habits
Solution Approach 1:
The patent replaces traditional mechanical data analysis methods with an artificial intelligence/gradient-boosted decision tree system. This substitution enables the detection of subtle patterns in customer interaction data that traditional methods miss, thereby improving prediction accuracy for customer acquisition behavior while accepting increased system complexity.
Solution Approach 2:
The patent transforms customer interaction data by creating multiple derived parameters including temporal patterns, interaction frequency metrics, and behavioral change indicators. These parameter transformations enable the AI model to capture subtle changes in customer habits, directly improving prediction accuracy for secondary account acquisition.
2Reliability
If existing adaptive techniques are used to predict customer behavior, then the implementation is straightforward, but the techniques fail to characterize likelihood of acquiring secondary accounts from primary account holders
Solution Approach 1:
The patent segments customer data into multiple temporal intervals (e.g., recent interactions vs. historical patterns) and applies different analytical treatments to each segment. This segmentation enables the gradient-boosted decision tree to capture evolving customer behaviors more reliably, specifically improving the prediction of secondary account acquisition from existing primary account holders.
Solution Approach 2:
The patent implements a dynamic prediction model that continuously adapts to changing customer interaction patterns. The gradient-boosted decision tree is trained on temporal data to capture evolving behaviors, making the prediction system dynamically responsive to changes in customer habits rather than relying on static historical averages.
3Productivity
If real-time prediction is implemented using AI processes, then the ability to offer targeted financial products is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary training of the gradient-boosted decision tree model on historical customer data before deployment. This preliminary action creates a pre-computed model structure that can then make real-time predictions with reduced computational overhead, balancing the need for targeted product offering efficiency with acceptable energy consumption during actual prediction operations.
Data Source
AI summary
The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of each of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit the output data to a computing system, and the computing system may transmit digital content to a device based on at least a portion of the output data.