Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
In this article, we’ll discuss the need for automation in mortgage document data extraction in detail and understand the role intelligent document processing technology plays in this sector.
Let’s get right into it:-
Document checklist for mortgage loans
Documents required for a mortgage loan origination varies from lender to lender but almost all of them initiate the process by requesting borrowers to fill up 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 are needed to provide proof of income. Some lenders may ask for profit and loss statements signed by certified public accountants and cash flow analysis forms as well.
2. Assets and debts
Lenders are required to calculate the debt-to-income ratio and check debt obligations for borrower applications. Factors such as monthly financial commitments, debts, and bank transactions - all have to 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, credit scores, and letters explaining cases where borrowers have defaulted, missed payments, or made late payments. Every organization has acceptable credit score criteria which must be met to satisfy their mortgage loan origination requirements and automated data extraction helps lenders in locating 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
Challenges involved with processing different kinds of documents
Mortgage services involve a number of routine tasks and have to follow a set of predetermined rules for verifying and validating applications. Traditional IT solutions and semi-automated/manual document processing systems suffer from a mix of digitized and manual processes which makes 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 high volumes of mortgage loan origination applications and these can number over thousands. Manual data entry and extraction has a margin 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 do background and financial checks on borrowers. Swift turnarounds by employees become difficult for this reason and manual data extraction can cause disruptions in mortgage workflows.
3. Document frauds and operational downtimes
Lenders have to make customers wait to get applications approved due to delays and operational downtimes. This is because there are many rule-based criteria to be followed when validating their documents. Mortgage lenders have to perform credit risk analysis of borrowers and analyze their submitted data which can take time. Document layouts and signatures have to be checked to ensure that applications are genuine and not forged. Add to this, the high volume of applications which means turnaround times are not fast.
How to automate mortgage document processing
Professionals who want to automate mortgage document processing should have a good understanding of their existing processes, systems, and data extraction workflows. Summarized below are important steps lenders need to take in order 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 features auto-classification capabilities for paper-based documents and digital files which means APIs can interpret a variety of document layouts and formats without human inputs. Users can upload different documents in bulk and save hours of time in not having to manually categorize and sort through themes.
2. Use data parsing rules for eliminating 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 take into account the different types of mortgages, conditions of sales and repayments, and checks for inconsistencies in values such as interest rates, loan repayment dates, etc.
3. AI and RPA for quick document processing
With the help of RPA, lenders are able to set specific rules for approving applications, categorize applications, or perform credit risk assessments. The scope of document processing automation isn’t just limited to standard documents such as forms but it extends to other semi-structured documents such as bank statements, and invoices as well.
Benefits of automated mortgage document data extraction
There are many ways mortgage lenders are using RPA, Artificial Intelligence, and automation workflows 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 for reviewing data and there are no chances for data redundancy either.
2. Mortgage fraud detection
Lenders can use Loss Origination Systems (LOS) and Data Analytics to spot, prevent, and identify cases of mortgage frauds. Automated mortgage data extraction makes it easier to whitelist 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 24x7 without needing breaks. All this translates to faster response times, streamlined application approvals, and happier customers.
4. Scalable and efficient
Users don’t have to fear disruptions in their existing IT processes when using automation solutions. Docsumo is versatile, adaptable, and works with legacy infrastructures without any issues. For organizations that are scaling up, they can do so in real-time since data is stored, processed, and managed on the Cloud.
5. Ensures regulatory compliance
Mortgage lenders have to meet compliance guidelines laid down by the state and government. RPA and automated mortgage data extraction tools ensure these are met by reducing operational risks, validating data, and ensuring applications are thoroughly audited before processing.