Annuity Product Comparison via XBRL Data Standardization
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Solution Overview
Problem
Current systems fail to accurately compare and evaluate guaranteed income products, such as annuities, from different providers due to variability in investment returns, fees, and risk factors, making it difficult for both financial advisors and clients to select suitable products that fit individual profiles.
Innovation Solution
A method using an XBRL parser to standardize and categorize financial data from various providers, creating a database for comparing guaranteed income products, calculating expected returns, and projecting income based on individual profiles, enabling ranking and recommendation of suitable products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual evaluation methods are used for guaranteed income products, then financial advisors can assess products, but accurate comparison and evaluation of products from different providers becomes impossible due to variability in investment returns, fees, and risk factors
Solution Approach 1:
The patent transforms unstructured product data into standardized structured data by changing the representation parameters of financial products. It uses XBRL taxonomy to convert varying fee structures, investment return profiles, and risk factors into uniform data formats with standardized parameters, enabling precise comparison while maintaining manageable system complexity through automated transformation rules
Solution Approach 2:
The evaluation system achieves universality by creating a multi-functional platform that can assess any guaranteed income product from any provider using a single standardized framework. The system simultaneously handles data collection, standardization, risk assessment, return projection, and comparative analysis, making it applicable to diverse products without requiring provider-specific evaluation methods
2Loss of information
If detailed product data is collected for accurate evaluation, then product comparison becomes possible, but data standardization and categorization across different providers becomes extremely difficult
Solution Approach 1:
The patent introduces XBRL taxonomy as an intermediary layer between diverse product data sources and the evaluation system. This intermediary standardizes data from different providers by mapping various fee structures, investment options, and guarantee terms to uniform categories and data elements, preserving complete product information while making it easily processable through automated transformation rules
Solution Approach 2:
The system segments product data into distinct standardized categories including fee structures, investment return components, guarantee provisions, and risk factors. By dividing complex product information into manageable standardized segments, the system maintains data completeness while facilitating efficient processing and comparison across different providers
3Adaptability or versatility
If standardized data collection is implemented, then product comparison becomes feasible, but the system requires complex XBRL parsing and data transformation processes
Solution Approach 1:
The system implements self-service by enabling automatic data transformation and standardization without manual intervention. The XBRL parser automatically converts unstructured product data into standardized formats, and the evaluation engine autonomously processes transformed data to generate comparisons and recommendations, reducing operational complexity despite the sophisticated transformation processes required
4Measurement precision
If comprehensive product analysis is performed including investment returns, fees, and risk factors, then accurate product ranking is achieved, but the evaluation process becomes time-consuming and complex
Solution Approach 1:
The system performs preliminary action by pre-processing and standardizing product data before actual evaluation occurs. XBRL transformation and data categorization are completed in advance, creating ready-to-analyze standardized datasets. This preliminary standardization enables rapid subsequent analysis and comparison, achieving comprehensive evaluation accuracy without time-consuming manual processing during the actual product selection process
Data Source
AI summary
A method and apparatus for acquiring and parsing data into categories responsive to receipt of at least one prospectus of a variable or non-fixed annuity contract for storage in an annuity master database. Once annuity contract data is received in the annuity master database, the data is analyzed and a recommended annuity contract value is forecast using one of trend-line projection and Monte Carlo simulation. A result is a filtering and listing of at least one recommended annuity or, further, a ranking of annuity contracts available from various carriers recommended for an annuity contract purchaser responsive to an annuity contract purchaser profile.