Emotion-Based Fraud Detection Using Deep Learning Models
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Solution Overview
Problem
Conventional fraud detection systems in customer service contact centers often miss fraudulent communications and incorrectly flag legitimate ones, due to their inability to accurately analyze the emotive content of customer interactions.
Innovation Solution
A computing system employing an emotion-based indexer deep learning model and an emotion classifier, which processes text, voice, and video communications to determine emotion factor values and uses an emotion variance model to identify patterns indicative of fraudulent behavior by comparing current communications to historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fraud detection systems analyze customer communications, then they can identify potentially fraudulent communications, but they miss fraudulent communications and misidentify legitimate communications as fraudulent due to inability to accurately analyze emotive content
Solution Approach 1:
The patent transforms communication data into emotion factor values that represent different emotional dimensions (e.g., joy, sadness, anger, fear). This parameter transformation enables the system to analyze the emotive content of communications by converting unstructured text into quantifiable emotional parameters that can be compared against fraud patterns.
Solution Approach 2:
The patent replaces conventional rule-based or statistical fraud detection methods with a deep learning-based emotion variance model. This substitution allows the system to automatically learn and recognize complex emotional patterns associated with fraud without relying on predefined rules, thereby improving both accuracy and reliability.
2Measurement precision
If fraud detection systems use traditional analysis methods, then they can process communications efficiently, but they cannot accurately distinguish between legitimate and fraudulent communications
Solution Approach 1:
The patent segments the fraud detection process into distinct functional components: an emotion analyzer that extracts emotion factor values from communication data, and a variance model that compares these values against historical patterns. This segmentation allows each component to specialize in a specific task, improving overall discrimination accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces emotion factor values as an intermediary representation between raw communication data and fraud classification. These emotion factors serve as a bridge that transforms unstructured communication content into a standardized format that the variance model can effectively analyze, thereby improving discrimination capability without requiring direct complex interaction between all system components.
3Measurement precision
If the system analyzes emotional content of communications, then it can improve fraud detection accuracy, but it requires processing and storing additional emotion factor data
Solution Approach 1:
The patent extracts only the relevant emotional dimensions from communication data through the emotion analyzer, rather than processing all possible features. By focusing on specific emotion factors (such as joy, sadness, anger, fear) that are most indicative of fraud, the system reduces the volume of data that needs to be processed and stored while maintaining high detection precision.
Solution Approach 2:
The patent performs preliminary analysis by pre-processing communication data into emotion factor values before the main fraud detection process. This preliminary transformation organizes the data in a way that facilitates more efficient subsequent analysis by the variance model, reducing the computational burden during actual fraud detection operations.
Data Source
AI summary
Techniques are described for determining a likelihood that a customer communication is fraudulent using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of a current communication associated with a customer, wherein each emotion factor value indicates a measure of a particular emotion factor in the current communication; classify, using an emotion variance model running on the one or more processors, the current communication into an emotional fraud category based on the set of emotion factor values for the current communication associated with the customer; and determine a risk score for the current communication indicative of a probability that the current communication is fraudulent based on at least the emotional fraud category for the current communication.