Dynamic Intelligence Cube Module for Real-Time Risk Data
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Solution Overview
Problem
Insurance providers face challenges in managing and analyzing risk data from diverse sources for dynamic business intelligence, particularly in creating real-time, custom views of risk exposure related to catastrophic events, which requires efficient data manipulation and combination.
Innovation Solution
A system and method for generating dynamic intelligence cubes that allow users to define custom cubes based on client portfolios and impact events, involving an intelligence cube module that receives user inputs for dimensions and boundaries, validates them, and populates the cubes in real-time within an impact-on-demand or mapping system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from multiple disparate sources with different formats, then comprehensive risk monitoring capability is improved, but data management complexity increases
Solution Approach 1:
The system segments data management by creating separate dimension tables and boundary tables that organize data from multiple sources into structured categories. Each table handles specific aspects of data organization, making the overall complex data management process more manageable and systematic.
Solution Approach 2:
The system introduces an intermediary data structure layer consisting of dimension tables and boundary tables that mediate between raw data from multiple disparate sources and the final risk assessment outputs. This intermediary layer standardizes and organizes the data before it is used for analysis.
2Adaptability or versatility
If ad-hoc re-organization of data is performed for dynamic views, then flexibility in data analysis is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-defining and pre-organizing data into dimension tables and boundary tables with established relationships. This preliminary structuring allows for rapid ad-hoc analysis without requiring time-consuming re-organization operations, as the data is already prepared in the needed formats.
3Measurement precision
If custom intelligence cube definitions are created for specific client portfolios, then analysis precision is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional intelligence cube framework that can be configured for different client portfolios and analysis requirements using the same core dimension and boundary tables. The system handles multiple specific analysis needs through a single unified structure, avoiding the need for separate complex systems for each client.
Data Source
AI summary
Techniques for dynamically and remotely generating a business intelligence cube from an impact-on-demand or mapping system include an intelligence cube module configured to receive a user indication of a client portfolio stored at a remote mapping system. The module may receive user selections of cube dimensions and boundaries of the client portfolio for inclusion in a draft business intelligence cube definition. The module may validate the selected dimensions and boundaries, determine the presence of any anomalies, and in some cases, automatically modifying the draft to resolve the anomalies. A validated, approved cube definition may be delivered to the mapping system for storage. The mapping system may, in real-time and based on a user request, populate the cube definition with a selected dataset and return the populated business intelligence cube for analysis and utilization in reports and other business intelligence tools.