Automated Data Extraction System for External Source Integration
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Solution Overview
Problem
Business processes face challenges in consuming data inputs from external sources due to varying formats and structures, leading to significant costs, resource investments, and time-intensive compliance testing, which increases processing, memory, and bandwidth usage.
Innovation Solution
A system for automated data extraction and adaptation that receives data inputs from diverse channels, determines data quality, reformats data using metadata, and generates machine learning analysis outputs to enhance input channels and comply with regulations like KYC and anti-money laundering laws, reducing the need for unified data structures and manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If external sources ensure data inputs are provided in the necessary data structure, then data quality and compliance are improved, but cost, technology investment, and resource requirements increase
Solution Approach 1:
The patent introduces an intermediary system that sits between external data sources and downstream platforms. This intermediary automatically receives data in various formats, validates it against schemas, transforms it to the required unified structure, and forwards it to downstream systems. This mediator handles the complexity of data structure enforcement, freeing external sources from implementing complex validation systems while ensuring compliance.
Solution Approach 2:
The patent replaces manual or mechanical data structure enforcement mechanisms with automated computational systems. Instead of requiring external sources to implement complex validation logic or manual review processes, the system uses automated schema validation, data transformation engines, and compliance checking algorithms that efficiently enforce data structure requirements through software-based mechanisms.
2Stability of the object's composition
If external sources implement compliance systems, then data structure standardization is improved, but processing, memory, and bandwidth usage increase
Solution Approach 1:
The patent implements preliminary action by pre-defining data schemas, validation rules, and transformation templates before data arrives. External sources only need to provide data in basic formats, while the system has already prepared the validation frameworks and transformation logic. This preliminary preparation reduces the computational resources needed during actual data processing, as the heavy lifting of structure enforcement is done in advance through schema registration and template creation.
3Reliability
If compliance testing is performed between external sources and business processes, then data integration reliability is improved, but testing time and processing intensity increase
Solution Approach 1:
The patent implements self-service compliance testing where the system automatically performs validation, transformation, and compliance checking without requiring external manual testing. The data validation engine autonomously checks incoming data against schemas, identifies compliance issues, and performs corrective transformations. This self-service approach eliminates time-consuming manual testing cycles while maintaining high integration reliability through automated consistency verification.
Data Source
AI summary
Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.