Data Connector Framework with Client-Built SDK Integrations
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Solution Overview
Problem
Existing fraud detection platforms face inefficiencies in integrating data from various data providers due to frequent format changes and updates, requiring excessive engineering resources and time for data connector development, which hinders live deployment.
Innovation Solution
A data connector integration framework that provides a software development kit (SDK) to institutional users, enabling them to build their own data connectors at the client device, allowing for on-demand integration and enrichment of data from desired data providers, reducing reliance on centralized updates and improving scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the fraud detection platform centrally manages and updates data connectors for all data providers, then data integration quality and consistency are maintained, but engineering resources and time consumption increase excessively
Solution Approach 1:
The patent segments the data connector management function by separating the framework (hosted on the platform server) from the data connectors (hosted on client devices). This allows the platform to provide a reusable framework while individual clients manage their own connectors, reducing the engineering burden on the platform for each new data provider integration.
Solution Approach 2:
The patent enables client devices to self-serve by hosting and managing their own data connectors locally. Clients can independently integrate new data providers using the provided framework without requiring platform engineering resources, thus improving productivity while maintaining integration quality through the standardized framework.
2Manufacturing precision
If new data connectors are developed for each new data provider or format update, then data intake accuracy is ensured, but development time and resource consumption increase
Solution Approach 1:
The patent creates a universal data connector framework that can handle multiple data providers and formats through a common architecture. The framework provides standardized interfaces and utilities that work across different data sources, allowing accurate data intake without developing separate custom solutions for each provider, thus reducing development time while maintaining precision.
Solution Approach 2:
The framework performs preliminary actions by providing pre-built utilities, standard interfaces, and common processing logic that handle routine data integration tasks. This preliminary preparation reduces the time needed to develop new connectors for each data provider while ensuring accurate data intake through proven methodologies.
3Adaptability or versatility
If data connectors are updated frequently to accommodate format changes from data providers, then data compatibility is maintained, but system stability and deployment complexity increase
Solution Approach 1:
The patent moves data connector management to a new dimension by hosting connectors on client devices rather than centrally on the platform server. This dimensional shift allows clients to update their own connectors independently without affecting system stability, while the framework provides adaptability through standardized interfaces that accommodate format changes from various data providers.
Data Source
AI summary
There are provided systems and methods for a data integration framework that provides an institutional or organizational user data enrichment capability locally. Specifically, instead of relying on the fraud detection platform to constantly updating and/or building new data connectors to intake data from updated or a new data provider, an institutional user, such as a financial institution, may receive a software development kit (SDK) from the fraud detection platform, using which the institutional user may build its own data connector deployed at the institutional user.