OCR Mortgage Underwriting: A Complete Guide to Using OCR for Streamlining the Process

Data extraction from borrower's bank statements, W-2 forms, tax returns, profit and loss statements, and identity documents is critical for risk assessment and accurate mortgage underwriting. However, it can be difficult for mortgage businesses to extract accurate data due to challenges such as data complexity, different formats, security and compliance issues. 

For mortgage lending businesses, Optical Character Recognition (OCR) automates end-to-end document processing workflows and captures data accurately. Implementing OCR streamlines underwriting processes and significantly reduces mortgage loan processing time.

This blog discusses the role of OCR in mortgage underwriting, the benefits and challenges of implementing OCR, the use cases and documents involved, and a step-by-step guide showing how to extract data from mortgage underwriting documents using OCR. 

What is OCR mortgage underwriting process?

Optical Character Recognition (OCR) is an automation technology that distinguishes individual characters in documents and converts them into machine-readable texts for easy sharing, analysis, and further processing. 

OCR in mortgage underwriting scans necessary documents, extracts relevant data, and converts them into editable and searchable texts. These advantages of OCR data entry helps to assess the borrower's creditworthiness and decide the loan amount in the underwriting process. 

The role of OCR in the mortgage underwriting process

OCR plays a crucial role in automating several repetitive mortgage underwriting tasks, such as data extraction from borrower’s financial and identity documents, data validation, and data entry. 

Moreover, advanced OCR solutions with machine learning (ML) and natural language processing (NLP) algorithms help with fraud detection, risk assessment, loan portfolio, and cash flow analysis. 

This automation expedites mortgage underwriting data extraction, improving productivity and reducing loan processing time. Advanced analysis detects high-risk borrowers and helps calculate accurate loan amounts, preventing financial losses for your mortgage lending business.  

Benefits of OCR in the mortgage underwriting process

1. Enhanced efficiency

Mortgage underwriting involves extracting data from numerous documents, such as bank statements, property and identity documents, and tax reports, which is a time-intensive process. However, OCR-based tools enhance efficiency by capturing data in less than a minute and allowing employees to concentrate on high-value tasks such as nuanced analysis and underwriting. 

2. Increased accuracy 

OCR software offers greater accuracy as it extracts data from mortgage documents without human intervention, reducing the chances of human errors. Additionally, OCR tools perform validation and verification processes with available government databases and internal computations to ensure the borrower's identity and financial data accuracy.  

3. Cost savings

As OCR tools can handle large volumes of mortgage documents without human effort, lending businesses can cut costs on hiring additional employees to meet the growing data extraction processes. Extracting accurate financial data helps lenders save costs by sanctioning loans only to eligible borrowers. 

4. Improved compliance and security

Advanced OCR solutions provide robust security features to protect borrower's financial data against unauthorized access, data breaches, and cybersecurity risks. It can capture up-to-date and relevant data from mortgage documents, helping your mortgage lending business adhere to changing compliance regulations and industry standards. 

5. Accelerated decision-making

Advanced OCR tools process documents in batches, and you can extract data from multiple bank statements and mortgage documents simultaneously. This reduces document processing time to seconds, letting underwriters focus on cash flow analysis, risk assessment, and loan amount calculations, accelerating decision-making and expediting loan approvals. 

6. Streamlined mortgage underwriting process

With advanced capabilities such as ML and NLP algorithms, OCR can streamline mortgage underwriting by automatically extracting and analyzing data from diverse documents. This automated data extraction and analysis streamlines the mortgage underwriting process. 

7. Improved financial analysis 

Assessing borrowers’ financial capabilities is a complex and time-consuming process. However, AI-powered OCR software simplify financial analysis by helping with automatic bank statement reconciliation, fraud detection, and cash flow analysis. 

This advanced analysis provides insights on borrowers’ spending patterns, loan repayment capabilities, and associated risks. 

8. Enhanced customer satisfaction

Borrowers expect seamless experiences and quick approvals and prefer lenders who can sanction loans in minimal time. 

OCR tools make it possible by swiftly capturing relevant data from pay stubs and bank statements, analyzing risks, and calculating loan amounts. 

This speeds up loan sanctioning, significantly optimizing customer satisfaction with AI and reducing your lending business's churn rate. 

Challenges of using OCR in mortgage underwriting the process

1. Document quality issues 

Challenge: OCR-based tools rely on the source data to capture accurate information, and poor document quality could lead to errors and inconsistencies. Ensuring document quality is a serious challenge as mortgage documents contain scanned and distorted images, noise, blur, skew, and unclear texts. 

Solution: You can use individual tools to remove skew and noise. Otherwise, the best solution is to invest in an advanced OCR tool with intelligent document processing that automatically performs preprocessing techniques to improve accuracy, save time, and enhance efficiency.

2. Diverse document formats

Challenge: Mortgage underwriting documents differ in their structures, formats, templates, and fonts, resulting in multiple variations that can make it difficult for OCR to extract data. 

Template-based OCR demands pre-defined rules with fields to locate relevant data and capture it accurately. However, creating templates for multiple variations in these documents can be time-consuming, which is another hurdle. 

Solution: The best way to overcome this bottleneck is to choose an AI-powered OCR solution over template-based OCR tools. Without human intervention, AI-based OCR tools automatically learn and adapt to diverse document formats, including unstructured document data extraction and templates.

3. Security concerns

Challenge: Extracting data from borrowers’ bank statements, tax returns, pay slips, and identity documents involves high-security risk as data breaches and cyber threats become more sophisticated. Exposure to such confidential information can damage a company's reputation, increase customer churn rate, and incur financial losses. 

Solution: Choose an OCR for mortgage lending with role-based access, cloud storage,  encryption, and password protection to safeguard borrowers' data against cyber attacks and breaches. 

4. Integration challenges

Challenge: How do you integrate data with legacy and loan origination systems (LOS) and ensure data integrity and accuracy? Capturing data from disparate and siloed sources and integrating them with accounting and risk assessment software solutions may lead to errors and inconsistencies. 

Solution: Invest in a robust data extraction tool that integrates with LOS and other mortgage software solutions in your tech stack to maintain data integrity across different systems. 

5. OCR accuracy

Challenge:  Standalone OCR solutions cannot preserve documents’ formatting without third-party software integrations. OCR struggles to extract accurate data from misaligned tables, line breaks, indentations, and graphs in the newly generated bank statements and tax returns. 

Overcoming these OCR accuracy issues is a critical challenge for mortgage lending businesses. 

Solution: Choose an advanced OCR solution that guarantees a 99%+ % accuracy rate with effective validation checks and data verification processes. 

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Use cases for OCR in the mortgage underwriting process

1. Document verification and authentication

OCR tools can automatically capture accurate data from borrowers' identities, proof of residence, bank statements, pay stubs, and W-2 forms. 

They can automatically validate the extracted data, including the borrower's name, bank account number, opening and closing balance, and identity details, with other available databases such as passports, driver's licenses, and tax reports.

This automated mortgage document processing verifies the borrower's identity accurately and helps lenders to segment low-risk borrowers and fraudsters. 

2. Credit scoring and risk  assessment

OCR tools can automatically intake data from credit bureaus, bank statements, utility bills, and social media profiles to analyze borrowers' income, credit score, assets, liabilities, existing loans, debt-to-income ratio, missed payments, and previous loan defaults. 

This standardized output helps underwriters to understand the borrower’s loan repayment capabilities and finalize loan approvals. 

3. Fraud detection

Manually detecting fraudulent activities in financial and identity documents may not be reliable, as Inscribe's Fraud Detection Report shows that less than 10% of document fraud is visible to the human eye.  

However, advanced OCR solutions like document AI with ML and NLP algorithms can effectively detect nuanced manipulations in mortgage documents, such as watermarks, security features, font size and style, image resolution, and logos.

4. Loan document classification and indexing

AI-powered OCR solutions identify the complexity, document type structure, and data format of mortgage and financial documents and automatically classify them into the necessary workflows for further processing. This saves time for employees, reduces processing time, and ensures efficient classification and sorting. 

OCR can make the documents storage and retrieval ready by understanding the texts within these documents and indexing them with specific tags and attributes. 

5. Compliance monitoring and auditing

OCR solutions automate compliance auditing and validation for your mortgage business by extracting relevant data such as transactions, credit scores, and debt-to-income ratios and validating them automatically. 

This accurate data ensures compliance with the Truth in Lending Act (TILA), Home Mortgage Disclosure Act (HMDA), and Anti-Money and Laundering Act (AML), preventing heavy penalties, fines, and legal issues. 

6. Document management

Capturing relevant data from borrowers' documents using OCR solutions and converting them into a standardized format ensures efficient document storage in a centralized location. With quick access to financial data, underwriters in an organization can collaborate efficiently. 

7. Customer onboarding and KYC compliance

Borrowers expect a smooth onboarding process from mortgage businesses; failing to provide this may result in loss of customers. The right OCR solution captures real-time data from borrowers' identity documents and verifies it with supporting documents to ensure authenticity. 

Conduct Know Your Customer (KYC) compliance with accurate income and identity data and offer borrowers a quick and frictionless onboarding experience. 

For instance, PayU, a multinational fintech company, streamlined customer onboarding for digital lenders using Docsumo.

We are using Docsumo’s APIs to automate data capture from bank statements and identity cards while onboarding customers. This has reduced the time our operations team spends on data entry by manifolds while providing a much better customer experience.

- Prashanth Ranganathan
CEO, PayU Credit

8. Loan portfolio analysis and reporting

OCR tools can extract accurate data from the borrower's loan portfolio, such as previous loan types, duration, outstanding amount, loan payoffs, delinquencies, and recoveries.

Machine learning and predictive analytics help uncover hidden insights for underwriters to determine whether the borrower can handle and repay this mortgage loan appropriately. 

Different types of data extracted from mortgage underwriting using OCR

Here are different types of documents that OCR can process and optimize mortgage underwriting: 

• Identity documents: Identity documents such as driver's license and passport provide a wide range of data such as borrower's name, age, address, and phone number. This data is critical for customer onboarding processes. 

• Property documents: The property's documents contain data about its size, location, potential risks, floor plans, damages, and current condition. This data shows the property's fair market value and helps mortgage lenders calculate accurate loan amounts. 

• Assets and debts: Assets, debts, and existing loan documents provide data about the borrower's debt-to-income ratio, monthly financial commitments, and debt obligations. Capturing this information is critical to confirming whether the borrower can afford the new mortgage loan. 

• Financial statements: Financial documents such as bank statements, profit and loss statements, W-2 forms, IRS forms, pay stubs, and income tax returns provide details about the borrower’s income, expenditure patterns, and monthly cash flow. They authenticate the borrower's employment status and help assess the loan repayment capabilities. 

• Credit documents: Credit documents such as credit reports and letters contain data about credit scores, missed payments, previous loan defaults, and late payments—-showing the borrower's creditworthiness and if they are eligible for the new mortgage loan.

• Legal and regulatory information: Accurate, relevant, and up-to-date data from loan application forms, credit reports, and financial statements helps mortgage businesses ensure compliance with standard laws, rules, and regulations. 

• Miscellaneous documents: Documents such as rental invoices, utility bills, divorce decrees, bankruptcy and foreclosure reports, green cards, visa forms, and child support documents show information about the borrower's employment and financial condition. Extracting this data helps segment deserving borrowers and calculate loan amounts.

How to extract data from loan documents using OCR

Here is a detailed step-by-step process for using OCR software to collect data from the mortgage documents for accurate underwriting: 

1. Document capture

Upload scanned copies of identity and property documents by capturing them using a high-resolution scanner. Automate ingestion of printed documents such as bank statements, tax returns and W-2 forms directly from your mail. 

2. Preprocessing

Preprocessing makes texts and tables evident for OCR to distinguish characters from the background. 

OCR performs advanced preparation techniques such as binarization, skew correction, noise removal, thinning and skeletonization, image scaling, right sizing and resolution, geometric transformations,  and contrast and sharpness adjustments to enhance mortgage documents' quality and ensure accuracy. 

3. OCR data extraction

OCR then proceeds to extract data from mortgage and financial documents and convert them into editable texts. It first extracts key-value pairs (a pair of information) such as bank name, borrower's name, age, address and phone number, and bank account number.

Advanced OCR solutions with ML algorithms and neural networks accurately detect tables and capture relevant data from them—including opening and balance balances, salary, tax paid, monthly loan repayments, and property details. 

4. Data validation

After data extraction, OCR tools validate the extracted data with predefined rules to detect mismatching fields. They effectively flag errors, redundancies, and missing values and send them to the underwriter for final validation and review. 

5. Data verification

OCR verifies the extracted data with available government databases and internal computations to ensure the authenticity of borrowers and identify errors. This verification provides error-free data and detects fraud effectively, reducing losses for mortgage businesses.  

6. Standardized output

Once the data extraction and validation are completed, download the extracted data in the preferred format. 

For example, Excel format would be suitable for performing complex calculations such as SUM, AVERAGE, and VLOOKUP and calculating mortgage loan amounts. Otherwise, JSON format would be helpful for integrating them into existing databases. 

7. Seamless integration 

Integrations with third-party business sources ensure error-free data flow between the software in your tech stack and downstream applications for further processing. 

The best OCR software for mortgage underwriting process data extraction

Docsumo, an AI-powered OCR software solution, simplifies your mortgage underwriting process with automated data extraction. It can automatically ingest bank statements, pay stubs, W-2 forms, proof of income letters, and employment contracts from email and scanner solutions using API integration.

Docsumo pre-processes relevant documents to ensure they are free of skew, noise, and blurred text. Pre-trained API models proceed to extract relevant key-value pairs automatically from financial and identity documents using OCR technology.

Docsumo uses advanced neural networks, image detection, and segmentation algorithms for complex tables to accurately locate tables and extract line items. While Docsumo extracts relevant data automatically, you can edit and customize the data fields catered to your mortgage business requirements.

Extracted data is validated data using Excel-like formulas to detect and correct errors. It alerts the underwriter of nuanced errors and manipulations, detects fraud efficiently, and reduces losses for your mortgage business. 

You can download the extracted data in your preferred format and integrate them into specific software solutions. 

Schedule a free demo with Docsumo to learn how it can automate end-to-end document processing workflows for your mortgage business and extract data with over 99% accuracy.