Transaction Timeline Image Scoring for Fraud Pattern Detection
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Solution Overview
Problem
Existing systems struggle to effectively evaluate system-generated historical transaction timeline images for predicting labeled behavioral similarities, particularly in fraud detection, due to the complexity of human behavior patterns and the need for advanced cognitive technologies.
Innovation Solution
A chart information metric framework is introduced to facilitate deep learning processes by providing chart evaluation factors, generating quantitative metrics using subjective and objective methodologies, and adjusting chart settings based on user feedback to improve pattern detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system-generated historical transaction timeline images are used for deep learning processes, then fraud detection capabilities are improved, but the complexity of evaluating and processing these images increases
Solution Approach 1:
The patent introduces chart information metrics as an intermediary that bridges the gap between raw timeline images and deep learning processes. These metrics serve as a mediator that translates visual chart characteristics into quantifiable data that can be effectively processed by machine learning algorithms, thereby improving fraud detection without directly increasing system complexity
Solution Approach 2:
The patent replaces manual or traditional mechanical evaluation methods with automated quantitative techniques. By using computational algorithms to calculate chart information metrics instead of human analysis or simple rule-based systems, the patent reduces processing complexity while maintaining or improving detection reliability
2Measurement precision
If quantitative techniques are used to generate chart information metrics, then pattern detection accuracy is improved, but the computational resources required increase
Solution Approach 1:
The patent segments the evaluation process into distinct quantitative metrics that can be calculated independently. By breaking down the complex task of pattern detection into separate measurable components (such as information density, temporal distribution, and spatial characteristics), the system achieves high accuracy while allowing for selective computation that conserves resources
Solution Approach 2:
The patent applies partial action by calculating only the necessary chart information metrics required for effective pattern detection rather than analyzing every possible attribute of the timeline images. This selective approach maintains detection accuracy while reducing unnecessary computational overhead
Data Source
AI summary
In an approach for evaluating system generated historical transaction timeline images for a computer vision deep learning process, a processor provides one or more chart evaluation factors for evaluating historical timeline images for a deep learning of patterns based on the historical timeline images. A processor generates a quantitative metric based on the one or more chart evaluation factors using a quantitative technique. A processor determines a score for an input timeline image based on the quantitative metric. A processor filters input space based on the score. A processor, in response to receiving a feedback, adjusts a chart setting based on the one or more chart evaluation factors.