Text Transcript Fraud Detection Using Vector Clustering
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Solution Overview
Problem
Existing fraud detection systems struggle to effectively identify fraudulent activities in customer service scams and review manipulation through text transcripts, as these schemes often go unreported and are difficult for human investigators to detect at scale.
Innovation Solution
An automated fraud detection system using vectorization techniques, nearest neighbor algorithms, and topic modeling to analyze text transcripts, transforming them into vector representations and clustering them to identify patterns indicative of fraudulent activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human investigators manually analyze text transcripts to detect fraudulent activity, then detection accuracy may be maintained through human judgment, but productivity and scalability are severely limited
Solution Approach 1:
The patent replaces the mechanical system of human investigators manually analyzing text transcripts with an automated computational system. The system uses vectorization to convert text into numerical representations, applies nearest neighbor algorithms for pattern recognition, and employs topic modeling to identify fraudulent themes at scale, eliminating the bottleneck of manual review while maintaining detection capability
Solution Approach 2:
The patent transforms text data from unstructured linguistic form into structured vector representations with specific dimensional parameters. By converting text transcripts into numerical vectors and analyzing them through mathematical operations (cosine similarity, Euclidean distance), the system enables automated processing while preserving the semantic information needed for fraud detection
2Productivity
If automated systems are implemented to detect fraud at scale, then productivity increases, but measurement precision and detection accuracy may deteriorate
Solution Approach 1:
The patent introduces vector representations as an intermediary between raw text transcripts and fraud detection decisions. The vectorization process converts text into numerical form that preserves semantic relationships, enabling automated algorithms to process the data while maintaining the nuanced information needed for accurate fraud identification
Solution Approach 2:
The system incorporates feedback mechanisms where the automated detection results can be reviewed and refined. The nearest neighbor approach provides confidence scores and similarity metrics that allow for threshold-based filtering and prioritization, enabling the system to maintain high accuracy while processing large volumes of transcripts
3Reliability
If comprehensive analysis of all text transcripts is performed, then detection completeness improves, but loss of time and computational resources increases
Solution Approach 1:
The patent implements a two-stage analysis approach where topic modeling first identifies transcripts containing potentially fraudulent themes, and only those flagged transcripts undergo detailed nearest neighbor analysis. This partial action strategy ensures comprehensive detection of fraudulent patterns while avoiding the time cost of analyzing every transcript in depth
Solution Approach 2:
The patent segments the fraud detection process into distinct analytical stages: topic modeling for initial screening, vectorization for representation, and nearest neighbor matching for detailed comparison. This segmentation allows the system to process transcripts efficiently by applying different levels of analysis intensity to different portions of the data
Data Source
AI summary
Devices and techniques are generally described for detection of fraud in text. In various examples, ground truth text data comprising a plurality of first transcripts related to fraudulent activity may be received. In some examples, a respective first data representation of each transcript of the plurality of first transcripts may be generated. Unlabeled text data including a plurality of second transcripts may be received. Second data representations of each transcript of the plurality of second transcripts may be generated. A plurality of clusters may be determined. A number of nearest neighbors that are associated with the plurality of first transcripts related to fraudulent activity may be determined for a first instance of the second data representations. A determination may be made that the first instance of the second data representations corresponds to a transcript related to fraudulent activity.