AI Curated Dataset System for Banking Data Inefficiency
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Solution Overview
Problem
Existing banking systems face inefficiencies in data gathering and presentation, leading to time-consuming tasks and redundant data access across multiple platforms, which consumes valuable resources and increases processing power requirements.
Innovation Solution
A computing system that scrapes data from internal and external sources, standardizes the data, applies AI algorithms to label entries, and compiles curated datasets, enabling real-time information retrieval and response generation through an AI interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is manually gathered from multiple platforms and compiled, then information can be obtained, but time consumption and resource usage increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-scraping, standardizing, and curating data from multiple sources before it is needed. Data is collected and processed in advance, stored in a standardized format with AI-generated labels and metadata, so that when a user needs information, it is already prepared and readily available, eliminating time-consuming manual gathering.
Solution Approach 2:
The patent introduces an intermediary system (the data curation platform with AI algorithms) that mediates between multiple data sources and the user. This intermediary automatically collects, standardizes, and organizes data from various platforms, then delivers it in a unified format, replacing manual information gathering and reducing time loss.
2Loss of information
If data is accessed independently from multiple platforms, then comprehensive information can be gathered, but bandwidth occupancy and processing power requirements increase
Solution Approach 1:
The system merges multiple data sources into a single standardized database. Instead of accessing multiple platforms independently, the patent consolidates data from various sources into one unified repository with consistent formatting and AI-generated labels, reducing redundant bandwidth usage and processing requirements while maintaining data comprehensiveness.
Solution Approach 2:
The patent creates standardized copies of data from multiple sources and stores them in a unified format. Rather than repeatedly accessing original sources, the system maintains curated copies with AI-generated metadata and labels, reducing bandwidth occupancy and processing power needs while preserving information completeness.
3Ease of manufacture
If manual data compilation and presentation creation is performed, then deliverables can be generated, but productivity and efficiency decrease
Solution Approach 1:
The system enables self-service by allowing users to query the standardized database and generate deliverables automatically through AI algorithms. Users can retrieve pre-curated data and have the system automatically create presentations and reports, eliminating the need for manual data compilation and presentation creation, thereby significantly improving productivity.
Solution Approach 2:
The patent changes the parameters of data delivery by providing pre-standardized, AI-labeled data that can be directly used for deliverable generation. This transforms the workflow from manual compilation to automated retrieval and generation, improving ease of manufacture and productivity simultaneously.
Data Source
AI summary
At least one aspect of this disclosure is directed to method of scraping, by a first computing system, one or more first data sources of the first computing system, and one or more second data sources of one or more external computing systems, to compile a first dataset, standardizing, by the first computing system, the first dataset to generate a standardized dataset, applying, by the first computing system, a first artificial intelligence (AI) algorithm to assign labels to data entries of the standardized dataset, compiling, by the first computing system, the standardized dataset having the labels assigned to the respective data entries in a database, receiving, by an AI interface of the first computing system, a query from a computing device, and generating, by the first computing system, a response to the query for delivering via the AI interface to the computing device.