Mortgage bankers face huge consumer demand to provide better customer experiences and make them seamless. Today, it is important to ensure that transactions processed are secure, verified, and audited. Income verification documents are used to check mortgage payees’ credentials and ensure they provide genuine details. Banks struggle to meet compliance requirements when processing customer data and personally identifiable information. This is where emerging technologies such as automated data extraction and intelligent OCR solutions come in.
Document fraud is a common problem faced by bankers and lenders who give mortgages to prospective buyers. The mortgage loan origination process entails various stages which a buyer goes through in order to get a loan from banks for buying homes, leading all the way up to the keys being handed over.
While a majority of submissions are genuine, sometimes users fabricate information in these documents to get loan approvals for mortgages. Fraudsters can hire third-party counterfeiting services to commit document fraud and many don’t need design skills to get past data security checks.
Changing the formatting of checks is a minor error which can be spotted but more sophisticated alterations such as edits to fonts, styles, spacing, etc., are often missed by the human eye.
A classic example is this black line for the activity summary. Notice how it doesn’t reach all the way to the end in the second image.
When processing huge volumes of documents, spotting these minute details becomes a challenge which is why mortgage employment verification Is needed. Many mortgage documents are photocopied and sent to banks and fintech companies. Sometimes the level of reproduction is such that it’s not visible to the eye that they’re not originals.
Another common type of fraud is robo signatures where officials who have worked at various banks end up signing multiple documents. These electronic signatures are stolen and used for producing false notaries, forgeries, and millions of fraudulent documents.
There are cases of non-authentic signatures being stamped on loan documents which malicious agents use to trick clients. Banks use employment verification for mortgages and analyze customer documents using AI, OCR, and machine learning solutions to spot such alterations and cross-reference details with original documents.
IRS Form I-9 proves the identity of employers and a recipient’s income status. Name, address, and social security number are the key details enlisted in these. Permanent Resident Cards, scanned passport pages, and employment authorization documents are needed as supporting documents while filling these forms up. Freelancers and self-employed individuals submit IRS Form 1099 Miscellaneous Income for mortgage applications.
Occasionally, banks require individuals to furnish a loss of income form to show changes in work status. Sometimes an employee gets fired, loses a job, or goes through a period of unemployment before getting hired again. Loss of income form is used for verification of employment and Section 2 of these forms is the area that explains unemployment reasons and details.
Banks and NBFCs require paystubs to be attested by current or previous employers since forging pay stubs is a common practice by criminals. Paystubs provide proof of employee earnings in a company or organization.
Income verification letters are used to show history of payments, salary, employment details, and additional references. Verifier’s name, company information, zip code, address, employment start and end dates, etc., are contained in it. Some states have a state-specific income verification form which banks request at the time of mortgage applications when these letters are not furnished. Income verification letters alone aren’t enough and they must be attested by a notary. Additional documents have to be submitted as proof of income for verification purposes such as tax returns, bank statements, and worker’s compensation letter.
Near-replica forgeries of documents used for fannie mae verification of mortgage make it possible for criminals to apply for loans and mortgages using fake identities and get away with financial crimes. Banks use automated data extraction from income verification documents for deeper analysis and get insights related to different forgeries committed.
By using intelligent OCR data capture and machine learning algorithms, it is possible to identify nuances in forgeries, examine document layouts, and spot missing details. AI technology combined with data extraction can help lenders prevent document frauds by marking false positives and cross-referencing databases that have analyzed original documents in the past.
Mortgage underwriters find it challenging to go through piles of documents and are usually responsible for approving or denying loan applications. As part of mortgage employment verification reviews, they have to do a cash flow analysis and verification modules can be created using automated document extraction software to streamline and speed up the process.
Docsumo makes it easy to extract data from Freddie Mac Form 91 and Fannie Mae Form 1084 and automatically enter those details into cash flow analysis forms. Here is a step-by-step overview of how to automate data extraction from income verification documents for analysis using Docsumo:
If you’re trying to automate data extraction from income verifications forms for quick and accurate analysis, using software like Docsumo is a reliable choice. Sign up for a free demo today to see how it works and get hands-on experience!
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
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.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.