Transaction Data Resolution System for Financial Analytics
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Solution Overview
Problem
Financial and transactional systems face challenges in processing and analyzing raw data from diverse and disparate sources due to inconsistencies and variations in data structures, limiting the exploration and analysis of unstructured and heterogeneous data.
Innovation Solution
A system and method for processing raw transaction records from multiple data sources, involving transaction pair generation, field extraction and resolution, and aggregation to eliminate ambiguities and generate analytics, including entity and location resolution, using modules like transaction pair generation, field extraction and resolver, and resolved record aggregation, employing techniques such as Hidden Markov Models and machine learning classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized processing is used for raw data from multiple data sources, then data processing can be performed in a unified manner, but data ambiguities and inconsistencies in unstructured and heterogeneous data limit exploration and analysis
Solution Approach 1:
The system segments the centralized processing into multiple distributed processing nodes that independently handle different data sources. Each node applies local data cleaning and standardization rules, then results are aggregated. This segmentation maintains unified processing capabilities while improving data consistency by allowing specialized handling of heterogeneous data formats at each segment.
Solution Approach 2:
The patent introduces intermediary data standardization layers that mediate between raw heterogeneous data from multiple sources and the centralized processing system. These intermediaries transform and normalize data before it enters the main processing pipeline, resolving ambiguities and inconsistencies while preserving the unified processing architecture.
2Adaptability or versatility
If data from diverse and disparate sources is processed, then comprehensive analytics can be generated, but inconsistencies and variations in data structures increase processing complexity
Solution Approach 1:
The system implements universal data abstraction layers that provide multi-functional interfaces for handling diverse data sources. These layers offer standardized methods for data ingestion, transformation, and querying across different source types, enabling broad data source compatibility while shielding the core processing logic from source-specific complexities.
Solution Approach 2:
The patent employs parameter-based configuration systems that allow flexible adaptation to different data sources through parameter adjustment rather than structural modification. Data source-specific parameters (formats, encodings, schemas) are configured dynamically, enabling the system to handle diverse sources with consistent processing logic while minimizing system complexity.
3Loss of information
If raw unstructured data is analyzed directly, then comprehensive data exploration is possible, but ambiguities in heterogeneous data limit effective analysis
Solution Approach 1:
The system performs preliminary data cleaning, validation, and standardization actions before the main analysis phase. This preliminary processing resolves ambiguities in heterogeneous unstructured data by applying domain-specific rules and algorithms, thereby improving measurement precision while preserving maximum information through reversible transformation methods.
Data Source
AI summary
A system and method for processing raw transaction records received from multiple data sources. The system and method receive multiple raw transaction records from multiple data sources. Transaction pair records are generated from the raw transaction records. Location and entity fields including raw information are identified from the transaction pair records. The raw location and entity information is resolved to generate resolved location and entity information capable of aggregation and further processing, such as the deriving of analytics.