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.

In this article, we 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.

To better understand the impact of AI on mortgage document processing, let's first examine the key stages and complexities within the broader mortgage and real estate industry.

An Overview of Mortgage and Real Estate Processes

Here is a detailed overview of mortgage and real estate processes involving lenders, buyers, lawyers, appraisers, and real estate agents.

1. Property Search

The buyer chooses a property based on online listings, newspaper articles, and references from real estate agents.

2. Pre-qualification

After negotiations and offering a price to the seller, the buyer contacts a lending company, and the loan officer reviews their credit history, bank statement, income, debt, etc., to recommend a suitable loan program.

3. Mortgage Application

The process where the borrower applies with personal and financial details and information of the property concerned.

4. Documentation Review

A loan officer reviews the borrower’s information across bank statements, pay stubs, credit reports, etc., to assess their assets, liabilities, and creditworthiness.

5. Title Search

Performed by a legal company to confirm the rightful owner while ensuring there are no legal disputes

6. Property Appraisal

A licensed appraiser performs a valuation of the property to determine if its true value matches the loan amount requested

7. Underwriting

The required paperwork and documentation go to the underwriter, who makes a final decision by evaluating the borrower's application and documents.

8. Loan Processing

After conditional approval, the loan processor collects any outstanding documentation, coordinates with third parties (such as insurance companies or appraisers), and prepares the loan file for final review. 

9. Loan Approval

Once all the conditions, including closing disclosure and loan estimates, are met, the lender issues a clear to close.

10. Closing

After contingencies are met, the transaction proceeds to the closing stage. At closing, the buyer and seller, along with their respective real estate agents and any necessary representatives, gather to finalize the sale.

Now that we understand the complexities of traditional mortgage and real estate processes, let's explore the specific types of documents that AI is successfully automating within this industry.

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 figure 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

With an understanding of the documents being automated, the next question that can come to your mind is how automated data extraction, powered by AI, translates into tangible benefits for both lenders and borrowers within the mortgage lending industry. Let's investigate.

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.

6. Reduces Loan Closing Time

IPA fast-tracks the mortgage processing lifecycle by reducing manual effort and increasing efficiency in documentation and verification. It automates and streamlines data extraction, validation, reviews, and underwriting tasks by identifying relevant information, even from unstructured documents like loan applications, lease terms, agreements, invoices, etc. 

7. Minimizes Cost and Resource Fatigue

According to Forbes, a leading institution continues to save 11 million USD every year on credit, verifications, and underwriting using an AI-based tool. 

By automating documentation and review, intelligent process automation tools reduce the reliance on manual labor and operational costs. It frees up real estate professionals, loan processors, and underwriters to focus on more strategic tasks such as quality control, cross-selling, and shaping personalized buying experiences.

8. Increases Business Prospects by Streamlining the Customer Experience

With well-trained AI-powered systems, you can keep track of sentiment analysis across digital and social shopping platforms to identify customer needs. Through real-time visibility, lenders can enhance the customer experience and foster loyalty by offering personalized account servicing. At the same time, agents can stay updated with market, inventory, and customer trends.

IPA also increases customer satisfaction through faster response rates, quicker information retrieval, and reduced processing time.

9. Simplifies Auditing by Enabling up-to-date Compliance

In 2021, JP Morgan was penalized $125 million by the SEC for insufficient record-keeping and communication in business transactions. This case highlights the costs of poor compliance. To address such challenges, implementing machine learning-based automation tools can be valuable. It creates a comprehensive audit trail, accurately classifies and stores information, and mitigates risks associated with inadequate record-keeping.

With a clear understanding of the advantages, let's get into the practical strategies and considerations for successfully automating mortgage document processing within your organization.

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.

To further illustrate the power of AI in mortgage and real estate, let's explore some real-world use cases of Intelligent Process Automation (IPA) within this dynamic sector.

Use cases of IPA in mortgage and Real Estate

1. Automated Document Classification and Extraction

You can streamline the document processing workflow by automating the classification and extraction of information from related documents. IPA leverages technologies such as OCR to convert scanned texts and images into machine-readable formats. It then analyzes the data to extract relevant fields such as name, address, loan amount, interest rates, etc.

It later classifies the documents based on their types and purposes. By training machine learning models on a labeled document dataset, IPA can learn to recognize patterns and automatically assign the appropriate category or label to incoming documents. For example, IPA can classify documents as loan applications, income statements, property appraisals, legal contracts, or tax documents.

2. Intelligent Chatbots for Customer Service

By leveraging the power of NLP, you can use the tool to automate communication with borrowers, providing timely updates on loan status, document requirements, and closing information. The AI-powered chatbots respond quickly and efficiently to customer queries and provide personalized recommendations.

Moreover, machine learning algorithms enable the chatbot to analyze user feedback, identify areas for improvement, and adapt to changing borrower requirements. 

3. Automated Underwriting and Appraisal Analysis

IPA automates underwriting by analyzing credit reports, income data, and property information. It uses predefined rules and algorithms to assess credit risk, property valuation, and loan-to-value ratios, helping underwriters make faster and more accurate decisions.

It can also perform quality control checks on appraisal reports to detect anomalies and potential errors and ensure their accuracy and reliability.

4. Predictive Analytics for Real Estate Investment

IPA leverages historical data and machine learning algorithms to generate insights on market trends, customer behavior, and portfolio performance. It enables organizations to make data-driven decisions, identify areas for process improvement, and provide customized recommendations to clients. Investors can gain valuable insights, make data-driven decisions, and enhance their real estate portfolios' overall performance and profitability.

5. Post-closing Quality Control

The technology reduces lenders' post-closing review and compliance costs by providing real-time reporting, automating data extraction, flagging potential issues, and streamlining compliance processes. These automated solutions improve efficiency, accelerate the review process, and facilitate prompt corrective actions.

Building on these real-world examples, let's now examine some of the top tools and technologies that are empowering lenders to automate their mortgage document processing workflows.

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

Docsumo offers flexible pricing plans to cater to various business sizes and requirements. 

• The free plan includes 100 pages per month, which is suitable for testing the platform's capabilities. 
• The Growth plan, starting at $299 per month, provides 1,000 pages per month along with additional features like API access and multiple-user support. 
• For organizations with higher volume needs, the Enterprise plan offers custom credits and tailored solutions.

Google Cloud Document AI

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

While these tools offer powerful capabilities, it's important to acknowledge the unique challenges associated with processing different types of mortgage documents. Let’s explore them in more detail. 

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 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. 

4. Complex Documentation 

Real estate and mortgage transactions involve paperwork such as deeds, contracts, disclosures, credit reports, and bank statements. Gathering, managing, and verifying an extensive range of documents requires hyper-focused vigilance, expertise, and support from resources. Such efforts usually take attention away from more strategic decision-making that can help the company grow and reap profits.

5. Delayed Closing

Document processing challenges can lead to unforeseen delays that affect loan closure and transaction timelines. Repetitive tasks take significant time and effort, including data extraction from listings, property documents, bank statements, pay stubs, credit history, valuation, and tax reports. 

If not handled systematically, prolonged documentation leads to closing delays, thereby frustrating both buyers and sellers awaiting the agreed-upon closing date stated in their contract. Buyers often pay higher interest and lose their mortgage affordability if there is a delay due to errors, inaccuracies, or missing documents. This impairs the customer experience, and real estate agents usually find themselves on the receiving end of buyer/seller mistrust.

6. Compliance and Regulation

Besides quick turnaround, buyers expect compliance with regulatory and compliance frameworks. Mortgage processors and real estate agents face challenges navigating the complex web of industry regulations and federal laws. It gets further completed as they need to maintain speed, precision, and accuracy in the documentation.

7. Data Errors and Inaccuracies

Data blunders cost billions, and there are enough instances to prove it. One of the most prominent examples is the 2008 US housing bubble. While several factors culminated in the disaster, there’s no denying the aspect of poor risk assessment, which relies heavily on error-free documentation.

8. Risk of Fraudulent Activity

Due to inefficient document processing, lenders often lose sight of errors and inaccuracies that could indicate possible fraudulent activity. Buyers can suffer substantial financial losses or become victims of identity theft, ruining their relationships with lenders and real estate enterprises.

9. Communication and Collaboration

The various data points emerging from loan processors, underwriters, borrowers, appraisers, real estate agents, and legal advisors require streamlined collaboration. The same becomes challenging to maintain due to communication delays or a lack of standardization for seamless data transfer.

Despite these challenges, navigating the mortgage loan process can be significantly streamlined with careful planning and understanding. Let's explore the key steps involved in securing a mortgage loan.

Procedure to Get You Through with Mortgage Loans

The mortgage loan underwriting process involves a series of steps that lead to loan eligibility. These are as follows:

1. Pre-approval

This is typically the first step in loan origination workflow. Mortgage pre-approvals are generated through a soft analysis of the borrower’s credit profile, which helps the lender determine if borrowers meet specified lender characteristics.

2. Shopping

Once the above step gets a nod, borrowers create a budget. It requires means of financial advisors and a mortgage loan calculator to manage the limits. The buyer may pick the items of their choice, followed by agents bidding on the offer.

3. Mortgage Application

The most critical step for the borrowers is they must present documents in front of lenders. These documents comprise asset, debt, income, and identity verification documents.

4. Loan Processing & Underwriting

The document verification part for loan approval may take some time. In this step, mortgage underwriters take debt obligations, pay stubs, and bank statements into account. They need to ensure whether the borrower can pay the debt or not.

5. Loan Approval

The loan acceptance may take a lot of time with human intervention prone to human errors.

To successfully navigate the mortgage loan process, it's crucial to understand the specific documents required by lenders for underwriting purposes.

Documents Required for Mortgage Underwriting

The entire procedure involving mortgage loans hovers around mortgage underwriting. Numerous documents are needed to ensure that a needful nominee gets the mortgage loan. 

In many cases, the papers required are:-

1. Income Verification Documents

The nominee must provide their income verification documents prior to mortgage filing, including different tax forms.

2. Identity Verification Documents

The nominee filing must also provide all the verified identities along with other documents.

3. Loan Application Documents

There are different forms for different loan types. These documents must be properly filled with exact details and no wrong entries. Any incorrect information may lead to further complications afterward.

Workflow to Process Your Mortgage Loan Documents

Mortgage document verification can be done in two ways:-

i) First is to go with manual inspection, which can cause accuracy as well as budget issues

ii) The second way is to use smart IDP solutions like Docsumo for automated data extraction and analytics.

Docsumo's intelligent OCR API helps with quick automated data extraction with over 99% field-level accuracy.

Let’s take an example of Fannie mae/Freddie mac form 710 and discuss the entire workflow in detail:-

Form 710, which is also known as the uniform applicant assistance form, is a type of mortgage loan record drafted by a lender to hold reasonable conditions applicable for mortgages or any other mortgage-related services. A common automated data extraction tool backed by templated-based OCR may find it difficult to extract data due to different variations in the form (due to structural and layout designs).

However, Docsumo frees up vital resources by employing intelligent document processing to automate Form 710 with 99% accuracy.

The steps involved in the process are as follows:-

1. Formal Detection

The first step of IDP form processing involves the identification of files. Most OCR works on images. So, this step focuses on converting other file formats into images which is essential to perform OCR. 

A typical code snippet representing the entire process would look like this:-

file_name = 'image_10.png'
img1 = np.array(Image.open(file_name))
text = pyocr.image_to_string(img1)

Want to know the result of the above code? Just type:-

print(text)

It prints the output in the runtime environment.

2. Pre-processing Images

This step involves scanning the image with 100% noise reduction. Noise can make it difficult for the user to identify the text from the background image. Even the blurring and smoothing of the image performed at the juncture removes outlier pixels from the image.

3. Data Extraction

The data extraction tools extract key-value pairs and tables from form 710. OCR is used to extract line-item data and key-value pairs:-

a) Table data extraction

Many OCR software tries to detect lines and other visual components to execute a proper table extraction. However, this character recognition alone is unable to extract data from tables as this approach fails to assign attributes based on context. That becomes one of the biggest challenges in document capturing. Modern computer vision and machine learning algorithms are used to extract line item data profoundly.

b) Key-value pair mapping

Template-based OCR can only pull out key-value pairs efficiently from the structured form. Another technique used is finding position references for different keys based on the anchor text in the form, which is mapped against respected values.

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 with Docsumo today to see how it improves mortgage document processing.