Multi-Layer Transaction Data Processing for Faster Feature Recognition
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Solution Overview
Problem
Financial institutions face challenges in efficiently extracting value from large and complex transactional data due to the inefficiencies of traditional data-processing systems, leading to high business costs and time spent on data integration and analysis across different departments.
Innovation Solution
A multi-layered data storage system is implemented to process and store user transactional data, generating sub-datasets with approximate feature attributes, allowing for standardized and traceable data elements, which can be accessed by users with tailored datasets based on their specific needs, and integrated with AI for advanced analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data-processing systems are used to analyze massive amounts of transactional data, then data users can access current and historical transaction data, but data users spend a significant amount of time researching, sourcing, and integrating data, leading to high business costs
Solution Approach 1:
The patent segments the data storage system into multiple layers (raw data layer, processed data layer, and result layer) to organize and manage transactional data systematically. This segmentation allows data users to access pre-processed and standardized data from the processed data layer without needing to integrate raw data themselves, thereby reducing data integration time while maintaining data analysis efficiency.
2Adaptability or versatility
If each department sources and integrates data independently for their specific business needs, then each department can get tailored data, but multiplying the time spent across many groups using the same data leads to high business costs
Solution Approach 1:
The system performs preliminary data processing and standardization in the processed data layer before data users need to access it. Data is pre-integrated, pre-cleaned, and pre-formatted with standardized schemas, so when data users request data for their specific business needs, they receive ready-to-use data without having to spend time on research and integration, thus eliminating redundant data research time while maintaining data customization capability.
Solution Approach 2:
The patent creates a universal processed data layer that serves multiple departments and business needs simultaneously. This layer contains standardized, integrated transactional data that can be accessed and customized by different departments for various analytical purposes, eliminating the need for each department to independently source and integrate the same underlying data, thereby reducing redundant data research time while maintaining adaptability.
3Quantity of substance
If traditional data storage systems are used, then data can be stored for later processing, but data users face issues with extracting value from big data due to inefficiencies in traditional data-processing systems
Solution Approach 1:
The patent adds a new dimension to the data storage system by implementing a multi-layered architecture with distinct processing stages. Instead of storing raw data and relying on traditional single-layer processing, the system creates intermediate processed data layers with standardized schemas and pre-computed features, enabling data users to extract value more efficiently by querying optimized data structures rather than processing raw data from scratch.
Data Source
AI summary
A computer-implemented method for performing a data processing and feature recognition processes. The method includes processing user transaction data associated with user transactional behaviors in a multi-layered data storage system, providing a user transactional behavior dataset at a processor, and collecting a plurality of user transactional behavior data associated with the user transactional behavior dataset.