Automated Loan Data Linking via Smart Key Generation
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Solution Overview
Problem
Existing systems face inefficiencies and errors in matching and linking data files with different identification characteristics, particularly in mortgage-related processes, due to manual key assignment and the use of third-party key generation solutions, which are costly and prone to data quality issues.
Innovation Solution
An automated system that uses machine learning algorithms to detect common attributes among data files and generate matching rules, allowing for the automatic linking of data files associated with the same loan product, regardless of their identification mechanisms, and provides a unique digital smart key for each data file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual key assignment is used, then flexibility in key generation is maintained, but processing efficiency deteriorates and human error increases
Solution Approach 1:
The system enables self-service automation where the automated underwriting system automatically generates and assigns unique identification keys to mortgage applications without requiring manual intervention from third parties. The system processes data files, extracts identification attributes, and assigns keys autonomously based on predefined matching rules, eliminating the need for manual key assignment while maintaining accuracy.
2Reliability
If third-party key generation solutions are used, then key generation capability is provided, but cost increases and data quality errors persist
Solution Approach 1:
The automated underwriting system performs multiple functions within a single integrated platform: it processes data files from various sources, extracts identification attributes, matches applications against criteria, and assigns unique keys. This multi-functional approach eliminates the need for separate third-party key generation solutions while improving data quality through centralized control and consistent processing logic.
Solution Approach 2:
The system incorporates feedback mechanisms where identification attributes extracted from data files are used to automatically adjust and refine key assignment decisions. The system can review and correct matching results based on predefined rules and data quality checks, ensuring high accuracy in key generation while reducing errors that occur with manual processes.
3Loss of time
If manual key assignment is used, then adaptability to different identification mechanisms is maintained, but processing time increases
Solution Approach 1:
The system performs preliminary extraction and standardization of identification attributes from various data file formats before the matching and key assignment process. By pre-processing and normalizing the data structures in advance, the system reduces the time required for subsequent matching operations and simplifies the overall process for users interacting with the system.
Data Source
AI summary
In an illustrative embodiment, an automated system links data files associated with loan submissions that have different identification attributes. The system may include computing systems and devices for receiving requests from a number of remote computing systems to identify loan products associated with a data file. The system can generate a matching input matrix comparing identification attributes from a first data file to identification attributes of candidate data files. The system can apply attribute matching rules to the matching input matrix to identify other data files that correspond to the same loan product as the first data file despite the data files having different identification attributes. The system can link data files corresponding to the same loan product within a data repository with a product linking key and output the linking key or other data for the loan product to a receiving computing system.