Automated Attribute Code Generation for Credit Bureau Logic
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Solution Overview
Problem
The complexity of evaluating business decisions based on vast amounts of credit data makes it difficult for companies to identify essential credit attributes for risk analysis and marketing purposes, leading to errors and inefficiencies in attribute specification and code generation across different systems.
Innovation Solution
An automated attribute generation system that includes modules for input, error checking, attribute verification, document generation, and code generation, which processes pseudo-code to produce attribute specification documents and platform-specific code sets, reducing errors and increasing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual attribute specification and code generation is performed across multiple credit bureau systems, then flexibility in handling credit bureau specific logic is maintained, but error rates increase and processing speed decreases
Solution Approach 1:
The system performs automatic error checking and validation of attribute specifications without requiring manual review. The error checking module automatically detects inconsistencies and validates attribute definitions against credit bureau conventions, enabling the system to self-correct errors and reduce manual intervention while maintaining high processing speed and reliability.
Solution Approach 2:
The patent replaces manual mechanical processes of attribute specification and code generation with automated computational systems. The attribute verification module automatically validates attributes against credit bureau conventions, and the code generation module automatically produces platform-specific code, eliminating manual errors and accelerating processing while maintaining flexibility through programmable validation rules.
2Productivity
If automated attribute generation is implemented, then processing speed increases, but complexity of the system increases
Solution Approach 1:
The system is divided into distinct modular components: an input module for receiving attribute specifications, an error checking module for validation, an attribute verification module for checking against credit bureau conventions, a document generation module for creating specification documents, and a code generation module for producing platform-specific code. This segmentation allows each module to perform a specific function efficiently, increasing processing speed while managing complexity through clear separation of concerns.
Solution Approach 2:
The attribute verification module serves multiple functions by validating attributes against conventions from multiple credit bureaus (Equifax, Experian, TransUnion) simultaneously. The system can process attribute specifications for different credit bureaus through a single unified interface, reducing the need for separate processing systems for each bureau and thereby managing complexity while maintaining high productivity.
3Measurement precision
If extensive credit data is analyzed for risk assessment, then accuracy of risk determination improves, but difficulty of evaluation increases
Solution Approach 1:
The system extracts and validates only the essential attributes needed for risk assessment from extensive credit data. The attribute verification module identifies and validates key attributes against credit bureau conventions, filtering out unnecessary data while maintaining assessment accuracy. This extraction approach reduces evaluation difficulty by focusing on critical attributes while preserving the precision needed for accurate risk determination.
Solution Approach 2:
The system transforms extensive credit data into standardized attribute parameters that can be systematically evaluated. By converting raw credit data into validated attributes with defined conventions and data types, the system maintains measurement precision for accurate risk assessment while reducing evaluation difficulty through parameter standardization and automated validation.
4Manufacturing precision
If attribute specifications are manually verified against credit bureau conventions, then accuracy of attribute definition is improved, but time consumption increases
Solution Approach 1:
The attribute verification module performs preliminary automatic validation of attribute specifications against credit bureau conventions before code generation. By pre-validating attributes, data types, and conventions, the system ensures definition accuracy is maintained while eliminating the need for time-consuming manual verification steps, thereby reducing overall time consumption.
Solution Approach 2:
The error checking module provides immediate feedback on attribute specification validity by automatically comparing definitions against stored credit bureau conventions. This feedback mechanism ensures high attribute definition accuracy by catching errors early in the process, while eliminating the need for iterative manual review cycles and thereby significantly reducing time consumption.
Data Source
AI summary
One embodiment of a system for automatically generating code for attributes includes a credit bureau data store that stores credit bureau convention information and an attribute generation system that communicates with the credit data store to detect rules associated with respective credit bureaus. The system may include an input module which receives, from a user system, a document comprising pseudo-code defining attributes including credit bureau specific logic; an error checking module which automatically performs error checking and provides error correcting information on the document; an attribute verification module which parses the document to identify attributes and automatically retrieves rules associated with the respective credit bureaus; a document generation module which generates an attribute specification document based on the retrieved rules as applied to the attributes in the document; and a code generation module which generates an executable code set based on the retrieved plurality of attributes and credit bureau specific rules.