A Lender’s Guide to Mortgage Document Processing

The sheer volume of documents lenders must collect and verify to process a mortgage application can be overwhelming. Handling the paperwork from bank statements and credit reports to pay stubs and tax returns may seem never-ending.

Efficient mortgage document processing can make the daunting process smoother for lenders and improve customer experience. According to Fannie Mae’s Mortgage Lender Sentiment Survey in October 2023, 73% of lenders adopt AI-powered tools to achieve operational efficiency.

This article will discuss mortgage document automation and its benefits for your lending business. We will also list the top 5 tools for efficient mortgage document processing and explain the automation process to make loan approvals efficient.

What is AI Mortgage Document Processing?

AI mortgage document processing uses artificial intelligence to automate and streamline loan processing workflows. 

AI and ML algorithms process documents like:

• Loan applications
• Income statements
• Property records
• Credit reports
• Bank statements
• Tax returns
• Employment verification documents

AI-powered mortgage document automation helps lending businesses streamline approval workflows and enhance efficiency. The automation tool automatically extracts, classifies, verifies, and analyzes information from mortgage documents submitted by a borrower. 

It reduces the need for manual data entry of borrower information in the lender's system and prevents related errors. Lenders benefit from processing loans faster and more accurately.

Types of Documents Automated by AI in Mortgage Lending

The documents required for a mortgage loan origination vary from lender to lender. Still, almost all of them initiate the process by requesting borrowers to complete a Uniform Residential Loan Application. 

This form contains details about your personal and financial information. Other supporting documents to be added along with this application are:

1. Income verification

Income verification documents prove the borrower’s repayment capacity. These include W-2 forms, pay stubs dating back to the last 30 days, copies of income tax returns, IRS Form 4506-C, etc. For self-employed individuals and freelancers, contracts and invoices spanning back to 2 years of work need to provide proof of income. 

Some lenders may also ask for profit and loss statements signed by certified public accountants and cash flow analysis forms.

2. Assets and debts

Lenders must calculate the debt-to-income ratio and check debt obligations for borrower applications. Factors such as monthly financial commitments, debts, and bank transactions must all be considered before approving mortgage loans. 

A cash flow analysis is done along with finding out additional details such as a borrower’s 401(k) plans, retirement and investment accounts, gift funds, and donor bank statements (for those accepting donations or charities as an additional income source) for further income verification.

3. Credit verification

Credit verification documents include credit reports, scores, and letters explaining cases in which borrowers have defaulted, missed payments, or made late payments. 

Every organization has acceptable credit score criteria that must be met to satisfy their mortgage loan origination requirements, and automated data extraction helps lenders locate key data in applications and figuring out if applicants are eligible. 

4. Other documents

Some additional documents may also be needed when processing mortgage loans. These documents are: 

• 12 months’ rental payment history
• Divorce decree, child support documents, and alimony information
• Bankruptcy and foreclosure records
• Green cards (for proof of employment), approved VISA forms, and employment authorization documents
• Utility bills

Benefits of Automated Mortgage Document Data Extraction

Mortgage lenders are using RPA, Artificial Intelligence, and automation workflows in many ways to speed up processing times without sacrificing quality or data integrity. 

Let’s take a look at some of the benefits of mortgage document data extraction for users:

1. Zero errors and improved accuracy 

Robotic Process Automation (RPA) and Intelligent Document Processing technologies employed in mortgage data extraction yield high accuracy rates and zero errors. Automation workflows require no human intervention to review data, and there is no chance of data redundancy.

2. Mortgage fraud detection

Lenders can use Loss Origination Systems (LOS) and Data Analytics to spot, prevent, and identify mortgage fraud cases. Automated mortgage data extraction makes it easier to allow prospective borrowers and ensures they are low-risk.

3. Enhanced customer experience 

Manual mortgage document data extraction and processing can take anywhere from 2 weeks to a month, depending on the speed and accuracy of employees. 

Automated data extraction gets the job done in minutes, and RPA systems can work 24/7 without needing breaks. All this translates to faster response times, streamlined application approvals, and happier customers.

4. Scalable and efficient 

When using automation solutions, users don’t have to fear disruptions in their IT processes. Docsumo is versatile and adaptable and works with legacy infrastructures without any issues. 

Organizations scaling up can do so in real time since data is stored, processed, and managed on the Cloud.

5. Ensures regulatory compliance 

Mortgage lenders must meet compliance guidelines that the state and government have laid down. RPA and automated mortgage data extraction tools ensure these are met by reducing operational risks, validating data, and thoroughly auditing applications before processing.

How to Automate Mortgage Document Processing

Professionals who want to automate mortgage document processing should understand their existing processes, systems, and data extraction workflows well. 

Below are important steps lenders need to take to automate mortgage document processing:

1. Auto-classification of mortgage documents with APIs

Machine learning algorithms and APIs can be trained to recognize different mortgage documents. IDP solutions for mortgage data extraction feature auto-classification capabilities for paper-based documents and digital files, which means APIs can interpret a variety of document layouts and formats without human input. 

Users can upload different documents in bulk and save hours of time by not having to manually categorize and sort through themes.

2. Use data parsing rules to eliminate inconsistencies

Irrespective of how automated data extraction is, errors can sometimes creep in when rules are not set. Data parsing rules create workflows for filtering and sorting through the data. 

These rules consider the different types of mortgages, conditions of sales and repayments, and checks for inconsistencies in values such as interest rates and loan repayment dates.

3. AI and RPA for quick document processing

With the help of RPA, lenders can set specific rules for approving applications, categorize applications, or perform credit risk assessments. The scope of document processing automation isn’t limited to standard documents such as forms but extends to other semi-structured documents such as bank statements and invoices.

Top 5 Tools for Automated Mortgage Document Processing

Mortgage document automation provides a competitive edge. AI document processing tools streamline workflows by reducing the time and effort required to manage the extensive documentation involved in mortgage applications. 

They help:

• Enhance accuracy
• Improve compliance
• Minimize operational costs
• Expedite lending processes
• Improve customer experience

Here are the top 5 tools you must try for automated mortgage document processing:

Docsumo

Docsumo uses AI-powered OCR (Optical Character Recognition) technology to accurately extract data from large volumes of structured and unstructured documents. It can identify and extract data from mortgage documents to reduce the time and effort required for loan processing. 

The platform preprocesses documents to reduce issues like skew, noise, or blurred text. Using AI, it accurately extracts key-value pairs from complex tables and financial documents. Docsumo is suitable for mortgage document automation as it efficiently handles diverse document formats.

Features

• Advanced OCR and AI help extract and interpret text from various document types and formats.
• Supports batch processing of various file types in multiple languages

Pros

• Supports a wide range of document formats
• Handles large volumes of applications quickly
• Achieves over 99% accuracy in data extraction

Cons

• Setup and training are time-consuming
• Pricing is high for small businesses
• Reporting capabilities need improvement

Pricing

• Free trial: 14-days
• Growth: $500
• Business: Custom pricing
• Enterprise: Custom pricing

Google Cloud Document AI

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Google Cloud Document AI is a powerful AI-based solution that helps automate mortgage document processing through ML and Generative AI. Its enterprise-ready API gathers mortgage documents from emails, cloud storage, and databases. 

Its robust ML models automatically classify documents and extract relevant data points. Google Cloud Document AI seamlessly integrates with existing mortgage workflows and systems.

Features

• Uses Generative AI for accurate document processing without model training
• Stores and manages scanned documents in the Cloud

Pros

• Pretrained models in 200+ languages
• Backed by Google's 25 years of OCR research
• Supports a broad range of document customization

Cons

• Expensive for small businesses
• Post-review latency of up to 30 minutes
• Requires technical expertise and lacks support

Pricing

Free credits: $300

Enterprise Document OCR Processor:

• $1.50 per 1,000 pages up to 5,000,000 pages/month
• $0.60 per 1,000 pages for 5,000,001+ pages/month

Add-ons: Pricing varies by product and usage

VaultEdge

VaultEdge uses ML, Generative AI, and LLM models to automate mortgage document processing. The software supports 900+ document formats and extracts 5000+ data fields, saving 90% of the time spent on data extraction. It is known for its speed and accuracy in mortgage document processing.

Features

• Automates mortgage document processing for around 500 document types to extract over 2,000 data points
• Offers open APIs that integrate seamlessly with Ellie Mae and Black Knight to comply with industry standards

Pros

• Flexibility to handle over 3 million pages daily
• Robust BCP/DR framework to ensure 100% uptime
• Stringent cybersecurity and risk mitigation with SOC II compliance

Cons

• Slower product innovation
• Less caring customer support 
• High pricing

Pricing

• One-time setup fee: $15,000
• Usage cost: $15 per loan file

Amazon Textract

Amazon Textract extracts text from tables and forms using ML. This powerful AWS data extraction tool processes text and handwriting from scanned documents with varying layouts. 

Amazon Textract enhances mortgage document processing workflows by integrating with AWS services, such as Amazon Comprehend for NLP and Amazon Augmented AI (A2I) for human review.

Features

• Allows customization of pre-trained queries to improve data extraction accuracy 
• Detect key-value pairs in document images automatically

Pros

• Processes dense text from tables and handwritten forms
• Intelligent search index to organize extracted text
• Automates data entry to Excel

Cons

• Does not extract custom fields efficiently
• No support for document fraud checking 
• Supports limited languages

Pricing

• Free tier: Free for 1,000 pages per month for 3 months
• Detect Document Text API: $1.50 per 1,000 pages up to a million pages in a month, $0.60 per 1,000 pages up to a million pages in a month
• Add-ons: Pricing varies by product and usage

Tesseract OCR

Tesseract is an open-source OCR engine that is a cost-effective option for mortgage document automation. It can be integrated into loan origination systems and other mortgage software to streamline the application processing workflow. 

Tesseract can accurately extract data from different document types and formats, including plain text, HTML, PDF, TSV, ALTO, and PAGE.

Features

• Compatible with several programming languages and frameworks, including Python.
• Uses a Long Short-Term Memory (LSTM) engine trained in over 100 languages

Pros

• Customizable
• Free and open-source
• Supports multiple languages

Cons

• Struggles with extracting data from complex or low-quality documents
• Requires technical expertise to implement
• Lacks built-in data validation capabilities

Pricing

• Open source: Free
• Custom: Based on implementation costs

Challenges Involved With Processing Different Kinds of Documents

Mortgage services involve several routine tasks and must follow predetermined rules for verifying and validating applications. 

Traditional IT solutions and semi-automated/manual document processing systems need a mix of digitized and manual processes, making mortgage processes increasingly fragmented.

Below is a list of key challenges involved with processing various mortgage loan documents in the industry:

1. High processing costs 

Lenders have to process large volumes of mortgage loan origination applications, which can number over thousands. Manual data entry and extraction allow for human error, and when mistakes are made, re-corrections are involved. 

This leads to more time and money being spent on employees to get the job done, which can get expensive in the long run.

2. Variety of documents 

Lenders receive both structured and unstructured documents along with mortgage applications. Data is pulled from multiple sources to perform background and financial checks on borrowers. 

For this reason, employees' swift turnarounds become difficult, and manual data extraction can disrupt mortgage workflows.

3. Document frauds and operational downtimes 

Due to delays and operational downtimes, lenders have to make customers wait to get applications approved. This is because many rule-based criteria must be followed when validating their documents. 

Mortgage lenders must perform credit risk analyses of borrowers and analyze their submitted data, which can take time. Document layouts and signatures must also be checked to ensure that applications are genuine and not forged. 

In addition to this, the high volume of applications means turnaround times are not fast.

Ready to Start Using Mortgage Document Automation?

Mortgage document automation streamlines lending processes for lenders of all sizes. By automating manual tasks like document processing, data extraction, and loan origination, lenders can increase efficiency and accuracy.

Docsumo provides advanced AI-powered OCR to extract data accurately from various document formats. It automates manual tasks to save time for more important tasks such as customer service and loan analysis. 

Book a free demo of Docsumo today to see how it improves mortgage document processing.