AI Process Predicting Customer Trading Cessation
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Solution Overview
Problem
Financial institutions face challenges in identifying subtle changes in customer trading habits that signal a transition from active to inactive trading, leading to lost opportunities and reduced revenue, as existing methods are ineffective in real-time monitoring of subtle changes in spending or savings behavior.
Innovation Solution
A machine-learning or artificial-intelligence process, such as a gradient-boosted decision-tree model, is trained using customer interaction data to predict the likelihood of engagement events, including cessation of trading activities, by analyzing profile, account, transaction, trading activity, and engagement data, enabling proactive engagement with customers to maintain their interaction with digital portals and trading activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track customer trading habits, then system complexity is reduced, but the ability to detect subtle changes in customer behavior deteriorates
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with an artificial intelligence-based predictive model. The system uses machine learning algorithms to analyze customer interaction data, trading activity, and engagement metrics, automatically detecting subtle behavioral changes that traditional methods would miss. This substitution of mechanical monitoring with intelligent algorithms resolves the contradiction by achieving high measurement precision through automated data processing and pattern recognition.
2Reliability
If real-time prediction of customer engagement cessation is implemented, then customer retention improves, but computational resource consumption increases
Solution Approach 1:
The patent implements preliminary action by training the predictive model in advance using historical customer data. The model learns patterns of engagement cessation beforehand, enabling it to make rapid predictions in real-time without requiring intensive computational resources during actual customer monitoring. This pre-training approach allows the system to maintain high reliability in predicting engagement cessation while reducing ongoing computational energy consumption.
3Measurement precision
If comprehensive customer data analysis is performed to predict trading cessation, then prediction accuracy improves, but data processing time increases
Solution Approach 1:
The patent applies segmentation by dividing customer data into distinct categories such as interaction data, trading activity data, engagement metrics, and demographic information. The predictive model processes these segmented data types through specialized analysis pathways, allowing comprehensive data analysis to achieve high prediction accuracy while reducing overall processing time through parallel computation of different data segments.
Data Source
AI summary
The disclosed embodiments include computer-implemented systems and processes that predict activity-specific engagement events using trained artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with an activity and a first temporal interval. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an engagement event associated with a cessation of the activity during a second temporal interval, which may be disposed subsequent to the first temporal interval and separated from the first temporal interval by a corresponding buffer interval. The apparatus may transmit at least a portion of the generated output data to a computing system, which may perform operations based on the portion of the output data.