NLP Engine for Mortgage Document Processing
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Solution Overview
Problem
The U.S. residential mortgage industry faces inefficiencies and customer experience decline due to its reliance on antiquated legacy technology systems, leading to increased costs, regulatory risks, and the inability to adapt to digital transformation, particularly in the mortgage servicing stage.
Innovation Solution
A natural language processing engine is implemented, utilizing a verb-based approach to extract and label text segments, generate node-tree structures, and apply weight-based relevance within a data-dictionary and knowledge graph, integrated with blockchain technology to streamline operations and enhance customer experience across the mortgage lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If legacy technology systems are used in mortgage servicing, then system stability is maintained, but operational efficiency deteriorates and costs increase
Solution Approach 1:
The system segments the mortgage servicing workflow into distinct automated stages: document intake, NLP processing, data extraction, validation, and action execution. Each segment handles a specific function independently, improving overall efficiency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent replaces manual mechanical processing of mortgage documents with an automated NLP-based system that uses computer vision and natural language processing algorithms to extract, validate, and process information from loan documents, eliminating manual data entry and reducing operational errors.
2Loss of time
If manual practices are used in mortgage servicing, then system simplicity is maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by automatically extracting and validating data from loan documents before they enter the servicing workflow. The NLP model pre-processes documents to identify key information such as loan terms, payment schedules, and borrower details, preparing data for subsequent processing stages.
Solution Approach 2:
The automated system performs self-service by independently completing document processing, data extraction, validation, and workflow initiation without human intervention. The NLP system autonomously processes mortgage documents and triggers appropriate servicing actions based on extracted information.
3Reliability
If third-party systems are integrated to address regulatory requirements, then compliance is improved, but system complexity and cost increase
Solution Approach 1:
The system employs a universal NLP processing engine that can handle multiple document types and regulatory requirements through a single integrated platform. The same core system processes various loan documents, generates compliance reports, and manages workflows for different regulatory bodies without requiring separate specialized systems.
Solution Approach 2:
The system incorporates feedback mechanisms where the NLP model continuously learns from compliance outcomes and adjusts its processing algorithms. Compliance validation results feed back into the system to refine future document processing and ensure ongoing regulatory adherence.
4Adaptability or versatility
If legacy infrastructure is used, then implementation simplicity is maintained, but adaptability to digital transformation deteriorates
Solution Approach 1:
The system is designed with dynamic scalability, allowing the NLP processing capacity and workflow automation to be adjusted based on varying mortgage servicing volumes. The system can dynamically adapt to new document formats, regulatory changes, and processing requirements without requiring complete system redesign.
Data Source
AI summary
An embodiment of the present invention is directed to a natural language processing platform that builds a Data Dictionary and a Knowledge Graph specific to a particular domain, such as a mortgage domain. The Natural Language Processor (NLP) Engine of an embodiment of the present invention is directed to maintaining an innovative hierarchy that specifies conditions and actions. Data extraction may be performed in a manner that keeps the hierarchy intact. By keeping the hierarchy intact, an output from a data-extraction process ensures that the vertical top-down placement of sentences (e.g., plain, bulleted, indented) is consistent with the input documents.