Adaptive Alert Delivery Engine for Financial Notification Optimization
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Solution Overview
Problem
Current alert systems in the financial services industry are unilateral, burdensome, and inefficient, failing to consider user preferences and usage patterns, leading to inadequate notification of account activities, increased financial risks, and missed deadlines, resulting in additional costs for both customers and institutions.
Innovation Solution
An alert system utilizing engines to determine the optimal type, time, and method of alert delivery based on customer data, including activity and preference data, with the ability to respond to customer interactions and adjust delivery accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alerts are sent using default settings and elementary methods, then the system complexity is low, but the customer awareness and response effectiveness are insufficient
Solution Approach 1:
The system monitors customer responses to alerts and uses this feedback to dynamically adjust alert delivery methods, timing, and channels. This creates a closed-loop system that continuously improves customer awareness based on actual response data, resolving the contradiction between reliability and complexity by making the system adaptive rather than static.
Solution Approach 2:
The patent transforms the static alert system into a dynamic one by continuously adjusting alert parameters based on customer behavior patterns and response data. The system adapts delivery timing, channel selection, and message content based on real-time customer interactions, thereby improving customer awareness while managing complexity through automated adaptation rather than manual configuration.
2Loss of information
If the system sends alerts without considering customer preferences and usage patterns, then the ease of operation is high, but the loss of information occurs as customers may not receive or notice important alerts
Solution Approach 1:
The system performs preliminary analysis of customer preferences and usage patterns before sending alerts, pre-configuring optimal delivery methods, timing, and channels based on historical data. This preliminary action ensures that alerts are delivered effectively without requiring complex real-time decisions, thereby reducing information loss while maintaining operational simplicity through pre-computed optimization.
Solution Approach 2:
The system automatically analyzes customer behavior patterns and self-adjusts alert delivery parameters without requiring manual intervention. The system serves itself by using its own collected data to optimize its operation, thereby improving alert delivery effectiveness while maintaining ease of operation through automated self-optimization rather than manual configuration.
3Reliability
If the system uses a send-and-forget method of alert delivery, then the device complexity is low, but the reliability of notification is insufficient as the system does not verify customer receipt
Solution Approach 1:
The system implements a feedback mechanism that monitors whether customers actually receive and respond to alerts. This feedback loop allows the system to verify notification delivery and adjust future alert strategies based on actual customer receipt and response data, thereby improving notification reliability while managing complexity through automated feedback processing.
Solution Approach 2:
The patent replaces the simple mechanical send-and-forget approach with an automated intelligent system that uses data analysis and adaptive algorithms to verify and improve notification delivery. This substitution introduces complexity but automates the verification process, making the system self-managing and thereby improving reliability without requiring proportional increases in operational complexity.
4Loss of time
If alerts are delivered at default times and through default channels, then the ease of operation is maintained, but the loss of time occurs as customers may not be available to receive or respond to alerts promptly
Solution Approach 1:
The system performs preliminary analysis of customer availability patterns and preferences before selecting alert delivery timing and channels. By pre-configuring optimal delivery parameters based on historical data, the system reduces response time while maintaining ease of operation through automated pre-computation rather than manual timing adjustments.
Solution Approach 2:
The system dynamically adjusts alert timing and channel selection based on real-time customer behavior patterns and availability data. This dynamic adaptation automatically optimizes response time by delivering alerts when customers are most likely to be available, while maintaining ease of operation through automated decision-making algorithms that replace manual timing management.
Data Source
AI summary
Systems and methods are disclosed for providing alerts to one or more customers at an optimized time and communication channel based on at least customer preferences, transactions, activities, usage patterns, and other information; and for allowing customers to directly respond and communicate feedback to alerts that are provided to complete various account actions, including to “snooze” one or more alerts.