Mortgage Loan Processing System
Industry
Banking and Finance
Headquarters
United States
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Background
Horizon Bank is a trusted financial institution specializing in a wide range of banking services, including mortgage lending. Operating across multiple regions, the bank processes significant volumes of borrower-submitted documents, such as tax returns and pay stubs, through the Encompass portal (or similar third-party tools). To maintain efficiency and ensure compliance with strict regulatory standards, the bank sought to modernize its document handling and data processing workflows.
Challenges faced
Manual Data Extraction Slowed Processes
The bank’s team manually extracted borrower information, such as income and tax details, from submitted documents. This manual approach led to delays, frequent errors and inefficiencies.
Inconsistent Data Handling
The absence of configurable templates for various document types resulted in inconsistent data extraction and mapping, impacting the accuracy of loan approvals.
Limited Data Mapping to Encompass
Entering extracted data manually into Encompass was labor-intensive. Key fields like gross income lacked automation, which slowed down loan processing and caused bottlenecks.
Manual Document Categorization
Without automated categorization of critical loan documents (e.g. tax returns, pay stubs), the bank faced delays in organizing and processing loan applications.
Lack of Automated Data Validation
The absence of an automated system for validating extracted data led to frequent errors, requiring additional resources for corrections and slowing down the loan approval process.
Defined Solution
Mortgage Document Categorization and Search Optimization
Documents such as tax returns and W-2s were categorized using OCR technology, enabling full-text search capabilities. This improved document organization and processing speed.
AI-Driven Data Extraction and Mapping
An AI-powered system extracted key information, such as borrower income and credit history, from documents and mapped it directly to Encompass. Configurable templates handled diverse document types, improving accuracy and reducing manual effort.
Configurable Document Templates
Templates were created for different document types, including tax returns and pay stubs, to standardize data extraction without requiring hardcoded fields. This approach ensured flexibility and adaptability in handling various documents.
Streamlined Document Verification and Change Tracking
The system tracked changes in borrower documents, such as income adjustments, and allowed users to verify and correct extracted data before submission to Encompass. This streamlined the verification process and reduced manual errors.
Automated Risk Detection and Flagging
The system identified and flagged inconsistencies, such as mismatches in income or collateral, in real time. Automated alerts ensured timely error detection, reducing compliance risks and expediting the loan approval process.
The Outcome
The Impacts
Improved Speed
Document digitization and search optimization accelerated processing and organization.
Increased Accuracy
AI-driven data extraction reduced manual errors, enhancing accuracy in loan processing.
Flexible Handling
Configurable templates provided adaptability for processing diverse document types.
Enhanced Loan Management
Document tracking improved transparency and management of borrower data.
Compliance and Risk Mitigation
Automated risk flagging reduced compliance risks and ensured regulatory adherence.
