Trade Interaction Chain Reconstruction via Relevance Scoring
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Solution Overview
Problem
Current interaction recording solutions provide a limited ability to reconstruct trade interaction chains, which are crucial for compliance with regulations like Dodd-Frank Wall Street reform and customer protection act, as they fail to accurately and comprehensively retrieve vocal and textual interactions related to trade deals.
Innovation Solution
A trade interaction chain reconstruction system comprising a preprocessing component for automatic transcription, information extraction, metadata association, and categorization, and an interaction chain reconstruction component that uses similarity measures to identify and reconstruct interaction chains by searching for anchor interactions based on detected words, entities, and metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional interaction recording solutions are used, then basic recording functionality is provided, but the ability to comprehensively and accurately retrieve and reconstruct trade interaction chains is limited
Solution Approach 1:
The system is divided into distinct functional modules: a retrieval component that searches for interactions using search criteria, a preprocessing component that performs transcription and information extraction, and a reconstruction component that generates the interaction chain. This segmentation allows each module to specialize in specific tasks, improving overall retrieval accuracy while managing system complexity through modular design.
Solution Approach 2:
The preprocessing component performs automatic transcription of audio interactions and information extraction (word/phrase detection, entity recognition) in advance, creating structured data that can be efficiently queried. This preliminary processing enables more accurate interaction chain retrieval without adding complexity to the retrieval operation itself.
2Reliability
If comprehensive information extraction and analysis are performed on all interactions, then accurate trade interaction chain reconstruction is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs information extraction and analysis selectively rather than uniformly on all interactions. The retrieval component uses search criteria to identify relevant interactions, and the preprocessing component processes these selected interactions to extract necessary information. This partial action approach maintains reliability for the specific interaction chain being reconstructed while reducing overall processing time and computational resources.
3Measurement precision
If manual review and selection of interactions is required, then accurate interaction chain identification is possible, but productivity and efficiency decrease
Solution Approach 1:
The system performs automatic transcription of audio interactions and automated information extraction (word/phrase detection, entity recognition, metadata association) without requiring manual intervention. The reconstruction component automatically generates the interaction chain by processing the extracted information. This self-service automation maintains accurate interaction chain identification while significantly improving retrieval efficiency and productivity.
4Ease of operation
If detailed metadata association and categorization are performed, then better interaction retrieval and chain reconstruction is enabled, but device complexity and processing overhead increase
Solution Approach 1:
The preprocessing component performs multiple functions in an integrated manner: automatic transcription, information extraction (word/phrase detection, entity recognition), metadata association, and categorization. By combining these functions into a single multi-functional component, the system achieves detailed metadata association that enables easy interaction retrieval while managing complexity through functional integration rather than separate systems.
Data Source
AI summary
The subject matter discloses a method for trade interaction chain reconstruction comprising: identifying a swap deal, the swap deal includes two or more of the received interactions and involves two or more participants; selecting a first interaction of the received interactions, said first interaction involves at least two participants of the two or more participants, said first interaction is stored on a computerized device; obtaining a first plurality of interactions of the received interactions that involve the at least two participants of the two or more participants; determining a first plurality of relevance scores between the first plurality of interactions and the first interaction; and associating interactions of the first plurality of interactions to be relevant to the swap deal according to the determined first plurality of relevance scores.