Dynamic Data Delivery Module for Secure Insurance Risk Mapping
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Solution Overview
Problem
Insurance providers face challenges in managing and delivering data from multiple disparate sources with different formats, making it difficult to create dynamic views and perform efficient data manipulation, especially when data combinations change frequently.
Innovation Solution
A dynamic data delivery module is implemented to automatically transform and validate source data into a compatible format, allowing for real-time delivery and storage in a mapping system, which geographically maps data to determine risk exposure, and includes secure data transfer mechanisms using public key cryptosystems and symmetric-key cryptosystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is manually managed and re-organized from multiple disparate sources, then data compatibility and quality can be maintained, but processing time and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically transforming and validating data from multiple sources before it reaches the impact-on-demand system. The data collection and management component pre-processes data from various disparate sources, converting them into a unified format and verifying quality, thereby eliminating the need for manual re-organization and reducing processing time while maintaining data compatibility.
Solution Approach 2:
The data collection and management component acts as an intermediary between multiple disparate data sources and the impact-on-demand system. This intermediary component handles the complexity of data format variations and quality issues, allowing the impact-on-demand system to receive consistently formatted data without direct exposure to the complexity of multiple source systems.
2Adaptability or versatility
If ad-hoc re-organization of data is performed for dynamic views, then data flexibility and adaptability improve, but system complexity and operational difficulty increase
Solution Approach 1:
The system implements dynamic data transformation capabilities that automatically adapt to different data formats and requirements. The data collection and management component dynamically adjusts transformation rules based on the source data format and the required output format, enabling flexible data handling without requiring complex manual re-organization processes for each dynamic view.
Solution Approach 2:
The data collection and management component serves multiple functions: it collects data from various sources, transforms data into unified formats, validates data quality, and manages data for dynamic views. This multi-functional approach reduces system complexity by consolidating what would otherwise require multiple separate systems into a single comprehensive component.
3Reliability
If data validation rules are manually applied, then data quality can be ensured, but processing efficiency and productivity decrease
Solution Approach 1:
The data collection and management component performs self-service validation by automatically applying validation rules to incoming data without manual intervention. The system self-verifies data quality against predefined rules and automatically corrects or rejects data that fails validation, ensuring data quality while significantly improving processing efficiency compared to manual validation methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and secure delivery of transformed and validated data to the mapping system, facilitating real-time risk exposure analysis and improving data management efficiency by ensuring compatibility and security.
Implementation Method 1
The dynamic data delivery module may be configured to automatically transform the source data from a first format into a second format, where the second format is compatible with the mapping system
Implementation Method 2
The dynamic data delivery module may be further configured to perform a validation of at least a portion of the source data based on at least one of the rules corresponding to the mapping system
Implementation Method 3
The mapping system may be configured to receive the transformed, validated source data from the dynamic data delivery module, store the data in a client portfolio, and geographically map contents of the client portfolio and impact events to determine risk exposure
Implementation Method 4
secure data transfer mechanisms using public key cryptosystems and symmetric-key cryptosystems
Implementation Method 5
secure data transfer mechanisms using public key cryptosystems and symmetric-key cryptosystems
Data Source
AI summary
In a preferred embodiment, a system for securely delivering source data to a destination system over a network includes a data manager system operatively connected to the network, where the data manager system includes circuitry configured to receive a public key from the destination system, generate a random session key, encrypt the session key with the public key using a public key cryptosystem, divide the source data into a number of chunks, compress each of the chunks, merge all of the compressed chunks into an assembled file, encrypt the assembled file with the session key using a symmetric-key cryptosystem, divide the encrypted assembled file into a number of parts, send each of the parts to the destination system over the network, and send the encrypted session key to the destination system over the network.