Color Encoding Transaction Data for Deep Vision Fraud Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Investigating transaction sets often involves diverse representations of transactions, making it challenging for reviewers to capture and understand context and patterns, especially in financial fraud detection, where traditional methods rely on numerical and textual information without effective visualization tools.
Innovation Solution
A computer-implemented method and system that maps categorical behavior transaction types to colors in a color coordinate system, scaling color component values to generate a pattern of colorized units over time, enabling deep visual learning and fraud detection modeling through color encoding of transaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional numerical and textual representations of transactions are used, then the data can be processed systematically, but the ability to directly recognize patterns and understand context is limited
Solution Approach 1:
The patent maps categorical behavior transaction types to colors in a color coordinate system, where different colors represent different transaction categories (e.g., red for suspicious, green for normal). This color encoding enables reviewers to instantly recognize patterns and anomalies visually without interpreting numerical data, directly improving pattern recognition accuracy while reducing understanding effort.
Solution Approach 2:
The patent transforms one-dimensional numerical and textual transaction data into two-dimensional visual representations with color and spatial dimensions. By plotting transactions on a color coordinate system with temporal spacing, the system adds visual dimensions that make patterns and relationships immediately apparent, resolving the contradiction between systematic processing and easy understanding.
2Loss of information
If detailed transaction representations are reviewed at varying levels of granularity, then understanding of transactions and patterns improves, but the time and complexity of review increases
Solution Approach 1:
The patent segments transactions by categorical behavior types and represents them as discrete color-coded units spaced according to time. This segmentation allows reviewers to quickly scan through segmented transaction groups rather than examining each individual transaction in detail, capturing overall patterns and context while significantly reducing review time.
Solution Approach 2:
The patent provides a visual overview that captures essential patterns and contexts without requiring complete detailed examination of every transaction. By displaying color-coded transaction types at appropriate time intervals, the system provides sufficient information for pattern recognition without the excessive time investment needed for granular review of all transactions.
3Measurement precision
If color component values are scaled to generate patterns of colorized units, then visual pattern recognition is enhanced, but the complexity of the encoding system increases
Solution Approach 1:
The patent scales color component values (such as RGB components) to generate distinct color patterns that represent different transaction categories and intensities. By adjusting color parameters systematically, the system enhances pattern detection precision while maintaining a manageable encoding framework based on standard color models, balancing improved detection with controlled system complexity.
Data Source
AI summary
A method, system, and computer program product for computer vision modeling are provided. The method identifies a set of transactions. A set of categorical behavior transaction types are determined for the set of transactions. The set of categorical behavior transaction types are mapped to a set of colors in a color coordinate system. The method scales color component values of the set of colors in the color coordinate system to generate a pattern of colorized units at intervals along a timespan of the set of transactions. The method generates a fraud detection model based on the set of transactions and the color component values.