Do You Have the Right Tools to Ensure Mortgage Data is Accurate?

When it comes to mortgages, accurate data is non-negotiable. It’s simply impossible to maintain regulatory compliance or confidently sell loans on the secondary market without it.  

However, ensuring data accuracy can be a daunting task. It takes a tremendous amount of time and effort to review loan documents and check and cross-check data manually. At the same time, the traditional technologies lenders and servicers have used to identify and capture data from loan documents are extremely limited.  

Adding to this data dilemma are two recent market developments that have made it even harder for lenders and servicers to ensure data accuracy. Originators are facing new QC requirements from Fannie Mae, while the hot MSR market and the growing potential for increased defaults is placing pressure on servicers to get data right. But as the French playwright Molière once noted, “The greater the obstacle, the more glory in overcoming it.”  


The Latest Push for Loan Quality 

Quality data demands on lenders rose significantly this year when Fannie Mae introduced new pre-funding and post-closing QC requirements that are expected to go into effect next month. The GSEs’ new policies require lenders to complete a minimum number of pre-funding QC reviews every month, plus lenders must now complete their post-closing QC reviews in 90 days instead of 120 days. 

While there has been some pushback on Fannie’s new requirements, they underscore the critical need lenders have for tools that enable them to quickly identify incorrect or missing data that could lead to loan defects and potentially costly repurchases. It also bears mentioning that Fannie Mae is far from alone in demanding higher loan quality. Investors and federal regulators are increasingly requiring it, too.  

Invariably, this means lenders need accurate data extraction technology that enables them to instantly identify, capture and extract data from any loan document. The problem? Most third-party providers of data extraction technology and legacy optical character recognition (OCR) tools have failed to keep up with the growing number of structured and unstructured loan documents. Some providers have been cutting staff in response to higher rates and lower loan volumes, too, so the problem continues to persist.  


Servicers Have Their Own Challenges  

The need for accurate data is not limited to lenders. On the servicing side of the business, high quality data is equally paramount—and growing more so. 

One reason for this has been the growing demand for MSRs, as many organizations have pivoted to servicing loans to counter decreased loan production. Ensuring accurate data is critical to quickly ingesting and analyzing loan files and auditing loan documents before integrating them into a servicer’s system. 

Data accuracy will grow significantly more important if mortgage defaults rise, too. Such a development is looking increasingly likely, since many new homeowners have found themselves stretched thin financially due to the recent rise in interest rates and inflation. In order to perform loss mitigation processes efficiently, including forbearance requests or loan modifications, servicers will need to ensure all existing loan data and the data from incoming borrower documents is accurate.  

Unfortunately, many data extraction technologies have proven no more effective for servicers than they have for originators—either because they are built on older technology or because their solutions are unproven. And ensuring data accuracy when exchanging MSRs or determining the proper loss mitigation option for a borrower in default is equally as intimidating when performed manually as it is for originators. Not only do manual processes create delays and potential errors when onboarding loans, but they also add to a servicer’s costs in what is already a cost-intensive business.   


Harnessing the Power of AI 

With the proper technology, any mortgage organization can ensure it is working with accurate data and is able to seamlessly identify potential issues before they become expensive problems. The right technology can also help lenders identify mismatched data and use it to help spot fraud. 

The key lies in leveraging modern, proven AI-powered data extraction technology that enables companies to effortlessly classify and extract data from diverse structured and unstructured documents with speed and precision. That is why an increasing number of lenders and servicers are turning to Paradatec’s award-winning AI-Cloud solution. 

Equipped with an impressive pre-built library of over 8,500 unique data fields found in loan documents, AI-Cloud harnesses advanced machine learning capabilities to swiftly and accurately transform the information trapped within documents into individual data elements. And it does so extremely accurately and reliably, so that clients can generate purified data to fuel their automation initiatives and gain valuable insights into their processes and strategic plans.  

Because it can pull data from over 850 known mortgage document types within seconds, AI-Cloud also empowers clients to confidently audit large volumes of loan files to meet Fannie Mae’s new requirements and post-closing deadlines. It also enables servicers to automatically audit data provided by prior servicers against incoming documents before ingesting MSRs into their system, thus minimizing errors and enhancing their overall customer experience. 


If recent market developments have you concerned about data accuracy, we can help. Just drop us a note at! 

Three Questions to Ask When Evaluating AI Document Automation Technology

As the housing market takes a breather, most mortgage and real estate companies are turning their attention to reducing costs. And few processes are more costly or time-consuming than document management.

When it comes to improving efficiency, shortening the time between receiving loan documents and making use of them stands at the top of the list. Fortunately, there are many document management software options for streamlining operations, including products that leverage optical character recognition (OCR), AI and machine learning technologies. Yet there are just as many broad and bold claims being made about them. How do you know what to believe? Or better yet, how do you test these technologies?

To help, we list three important questions to ask when evaluating these claims. Before you begin comparing vendors though, you’ll need to ask yourself a few questions.


Know Thyself

Mortgages may be a commodity of sorts, but every company is unique in terms of their business goals and how they get things done.

Before looking for document analysis technology, first ask yourself, “What specific problems are we trying to solve?” To answer this question, you must know what your requirements are on a high level. What workflows and documents are you using? What data needs to be extracted? And how do you intend to use that data? Which systems will be involved?

You’ll also need to know what your decision factors are. For example, how much are you prepared to spend on implementation and ongoing cost of ownership? How quickly do you plan to implement the solution? Is there an upcoming deadline for an audit you need to prepare for?

Once you know what your needs are, you can evaluate vendors by asking the following three questions:

  1. What will I actually get?

Simple question, right? Not exactly. If you’ve done any research into document technology vendors, you’ve encountered mountains of happy marketing lingo and hyperbole. What you need are facts, so this question demands a few follow up questions.

For example, will they do a live demo with you? Will they use documents you provide? What results will you get, and how are they measured? How granular are those results, how confident can you be with them, and what tools help you evaluate them?

Keep in mind that the mortgage and real estate markets aren’t static. What works today may not work as well tomorrow. So, what is the vendor’s approach to confirming results on an ongoing basis? The minute you hear, “you can set it once and forget it,” it’s probably time to look elsewhere.

  1. How does the system improve itself?

Now we’re getting into the nitty-gritty. The key here is understanding if and how results are improved over time, which is what you really want. You’ll also want to know how the accuracy of the results are measured and whether those measuring tools are available to you.

If you find the vendor uses machine learning, deep learning, transfer learning or convolutional neural networks to constantly improve results, you may be on the right track. If the vendor is using these technologies in a layered approach, that’s even better. But then you should ask whether the vendor built these technologies or licenses them. If they’re licensed, the vendor may not be able to explain how the system improves itself.

  1. What can I do with the results?

This is the time to find out whether your needs will be simply met or surpassed. For example, how easily are the results available for you to use with other systems, such as your system of record, data visualization software or reporting tools? What can you do to improve the results? How easy is it to implement changes to increase the number of documents recognized or the amount of data that can be extracted?

Finally, you’ll want to know how proactively the vendor adapts to change. After all, new document types and data needs are constantly evolving with new lending requirements, investor guidelines and market demands. How often is the technology updated? How frequently does the vendor make new releases available? How are they preparing for anticipated market changes? Are they willing to spend the time with you in a true partnership?


Gauging Future ROI

If you do this exercise thoroughly, you’ll have all the information you need to make a smart decision. Ideally, you‘ll be able to gauge future ROI in the form of quantifiable and qualitative savings.

Quantifiable savings is determining the amount of time in human process hours you’ll save by leveraging the solution, as well as how much you will shorten process cycles by reassigning staff to other areas. Qualitative savings can’t be measured as easily, but are equally valuable. It’s what you gain through outcomes such as enhancing risk management, using extracted data to feed data analytics initiatives and improving the customer experience.

At the end of the day, finding document technology that fits and is “real” is not an easy task, but it’s an essential one. At Paradatec, we’re always ready to answer your questions about our AI-based document technologies—plus we have real case studies showing they work. To learn more, please contact us at

Three Ways AI and the Cloud Help Lenders Reduce Costs

Three Ways AI and the Cloud Help Lenders Reduce Costs

The consensus is out—industry experts say 2022 is going to be a great year for purchase loans, possibly even a record breaking one. And looking around, it’s hard to argue. Rates remain historically low and the economy continues to rebound from the COVID-19 pandemic.

Yet appearances can be deceptive. The reality is that mortgage lenders will have more challenges in the year ahead than they’ll know what to do with, perhaps none larger than the cost of loan production. In fact, according to the Mortgage Bankers Association, independent mortgage bankers saw their expenses rise from $8,668 in the second quarter of 2021 to $9,140 in the third quarter—the second highest amount on record.

Of course, lenders can only do so much to manage costs. But one of the most effective is cloud-based, AI-driven document automation. Here are three reasons why:

     1. Faster Processing

The MBA is currently projecting that purchase loan volume will grow to a record $1.725 trillion next year. However, purchase loans are invariably more complex and therefore more costly to produce than refinances. Lenders must also deal with a growing number of compliance requirements and a longer sales cycle driven by low housing inventory.

Lenders may not have much control over these obstacles. But they can get a handle on one of the largest cost centers involved in mortgage production: document management. In fact, a growing number of lenders are leveraging AI technology that automates mortgage document indexing and data capture by being able to read an entire page of information in a fraction of a second.

By instantly extracting data from a borrower’s pay stubs, tax documents and bank statements, AI-based document automation is already helping lenders accelerate the underwriting process and free up pipelines. In post-closing, it can also verify all key data and signatures in a loan file and extract critical data for federal reporting requirements and custom reports in just minutes.

     2.  Better Scalability

Sadly, the majority of document processing providers still augment their technologies with significant human assistance. It’s very challenging to scale any technology that also depends on people—especially if it involves scaling your own team, which is too often the case.

In fact, several of our clients came to us after attempting to use optical character recognition technology (OCR) to capture loan data. But because these tools were not nearly as fast or accurate as they were led to believe, they needed to have their teams do more of the work as they attempted to scale their operations.

The good news is that modern AI technology that automates document processing requires little and sometimes no human involvement. That means lenders don’t have to hire additional processors as purchase loan volumes grow. Even better, this technology can be hosted in the cloud, where there are practically no limits to its scalability.

     3.  Zero IT Costs

Today’s AI-based document automation technology isn’t just faster and more scalable, but lenders don’t have to sacrifice their own technology resources to get it, either. There’s no need to install any software, and no need to pour extra resources into data storage or IT support.

Paradatec’s AI-Cloud is a perfect example. It’s easily the fastest, most powerful AI-based document indexing and data capture automation technology on the market, and incredibly easy to implement and use. All lenders need to do is upload any number of loan files to a secure unique website address and our technology does the rest.

AI-Cloud delivers the same powerful, AI-based text analysis and machine learning tools that are being used to process more than two million loans a year, helping lenders convert information trapped inside loan documents into pure streams of actionable data. It also includes our AI library of more than 850 loan documents, which potentially enables lenders to start automating document processes in as little as a few hours.

Unfortunately, we’re kind of alone in what we’re able to do. Many technology and service providers claim to provide AI-based document automation. But when it gets down to brass tacks, there’s no “there” there.

That’s why we offer free blind tests of AI-Cloud—so you can see for yourself. Or just drop us a note at, and we’ll be happy to show you how to reduce your loan production costs next year—and increase your potential.


Taking HMDA Reporting from Hassle to Harmony

Taking HMDA Reporting from Hassle to Harmony

There are few regulations more important in the mortgage industry than the Home Mortgage Disclosure Act (HMDA). For more than 40 years, the law has protected consumers from unfair lending practices. But it has also placed more work on lenders’ plates, especially those without the proper tools in place.

Thankfully, when the COVID-19 pandemic hit in March 2020, the Consumer Financial Protection Bureau gave lenders flexibility over reporting their quarterly HMDA data. This was a huge relief to lenders struggling to make the shift to remote work, and it also kept the housing industry chugging along. But it was only temporary.

Now that pandemic lockdowns are lifting, the CFPB is once again requiring lenders to file quarterly HMDA reports, and it has promised to enforce this requirement with stiff penalties. First quarter 2021 HMDA reports are due May 31 and second quarter 2021 reports are due on August 30. This can have significant consequences for lenders that put off investing in the proper HMDA reporting tools.

For example, federal regulators have the power to test any loan samples in a lender’s Loan Application Register (LAR) they want to. That’s a huge problem for lenders that rely primarily on staff and manual tasks for reporting HMDA data—especially today, when most lenders are receiving record numbers of loan applications. The fines for filing false or erroneous HMDA data are steep, too.

Fortunately, there is a way for lenders to meet CFPB reporting requirements with little effort.

Today’s document processing technologies are capable of automating the laborious task of identifying the final versions of crucial loan documents that contain key data points subject to HMDA. This is crucial, because a significant number of HMDA reporting errors are caused by lenders pulling data from documents that aren’t final.

Using AI-based document classification and data extraction tools, lenders can quickly scan hundreds of loan files, capture applicable HMDA data, and place it into a spreadsheet or XML file for accurate LAR reporting. It all happens in seconds, with little to no human oversight. These tools are infinitely scalable as well, which makes them invaluable when dealing with large spikes in application volume.

Modern document processing technologies can deliver other benefits as well. Because they generate highly accurate, actionable data, lenders can apply data analytics and other tools to gain better insights into their business practices and identify areas for improvement. They can also use the same technology for any kind of audit or for custom reports. This enables their staff to spend less time staring at documents, typing in data points, and performing other manual tasks that are highly prone to error.

Even better, lenders can access HMDA reporting technology in the cloud, with just a web browser and a link for uploading their files. This means lenders don’t have to spend a penny of their own money on IT costs to access technology that enables them to stay compliant.

The bottom line: HMDA reporting is serious business. But meeting the CFPB’s reporting requirements doesn’t have to cost an arm and a leg, nor does it have to distract your team from doing what it does best—growing business and making money.

If you would like to see how Paradatec’s automated document processing and data extraction technologies can streamline your HMDA reporting and ensure more accurate, higher quality data, simply email us at We are happy to show you how easy it can be!

Taking Flight in the Cloud

Taking Flight in the Cloud

 It’s hard to imagine a faster way to process loan documents than through automated mortgage document indexing (ADR) and automated data capture technologies, which are capable of “reading” entire pages of information in milliseconds. In fact, our own automated, artificial intelligence-based document classification and data extraction technology is able to process 25,000 loans in two days or less.

But there is a way to process loans even faster. We know because we’ve made it happen.

The Beauty of the Cloud

 Previously, document processing technology required lenders and servicers to install software behind their own firewalls, either through a private data center or within their own private cloud environment. By accessing these technologies directly from the provider’s fully hosted, secure cloud environment, lenders and servicers no longer need to spend any money on hardware, storage, or IT support just to process loan files.

And they’re able to process loans much faster, too. When placed in a cloud environment, the same document processing technologies can process portfolios of one million loans or more in the cloud in as little as 30 days. That’s several times faster than hosting these technologies yourself.

This is exactly what we’ve been able to achieve through AI-Cloud, our cloud-based platform hosted by Amazon Web Services (AWS). AI-Cloud delivers the same powerful, AI-based text analysis, optical character recognition (OCR) technology and machine learning tools our clients have used for more than 30 years to convert data trapped inside static loan documents into actionable data. AI-Cloud also includes our auditing technology, which breaks down every loan into more than 800 specific document types, completely eliminating the need to perform manual filename mapping during loan onboarding.

Already, AI-Cloud is being used by lenders and servicers for faster loan originations, mortgage servicing rights (MSR) onboarding, portfolio cleanup and more. The best part? It couldn’t be easier to use.

How it Works

To submit loans to AI-Cloud for processing, all a company needs to do is to set up an online account, which triggers the creation of a private “tenant” for its documents. The tenant keeps both the storage and processing of the company’s documents separate from other companies.

The company then gets a unique website address it can use for submitting batches of work, whether it’s a single loan file or an entire portfolio of loans.  However, there’s also the option to create a simple web services application programming interface (API) that enables a company to submit loans through one or more of its applications as encrypted files.

Companies can track the processing of their loans through a web page created just for them. Afterwards, they can verify any uncertain data elements—typically very few—directly online through a unique user interface, or Paradatec can perform the data purification itself.

Get Ready for Liftoff  

Make no mistake, automating loan processes to produce higher quality loans at greater speeds and fewer costs is no longer a competitive advantage—it’s essential. That’s especially true today, as lenders begin transitioning into a more purchase-oriented market, and as servicers face greater scrutiny from the Consumer Financial Protection Bureau.

The bottom line is that there is no faster way to process documents than through the cloud, which allows lenders and servicers to maximize their resources while focusing the bulk of their attention where it belongs—on their core businesses.

Interested in seeing how fast document processing can really get? It costs nothing to take a free test flight on AI-Cloud. With a sample of your mortgage files, AI-Cloud can immediately provide a fully indexed and bookmarked PDF for each loan file, with extracted data elements highlighted on each source document—all without any effort on your part.

Book your flight today by dropping a note at



Northpointe Bank Case Study

Northpointe Bank Case Study

Northpointe Bank

This nationwide correspondent lender needed a solution to replace its manual loan indexing and data capture processes, which were prone to error and creating excessive labor costs.


After an exhaustive evaluation process, Paradatec was determined to be by far the fastest OCR and ADR technology on the market. The company’s pre-built mortgage logic understood the vast majority of loan document types and variations, and its library of 8,500 data extraction fields supported NorthPointe’s correspondent lending processes.


 Since implementing Paradatec, Northpointe has experienced significant reductions in labor costs, error rates, and processing times. It has since taken advantage of the large volume of highly accurate and valuable data generated by Paradatec to implement sophisticated business rules to streamline loan production.

How New is AI in the Mortgage Industry, Really?

How New is AI in the Mortgage Industry, Really?

 Artificial intelligence, or AI, has never been more popular in the mortgage industry than it is today. Ten years ago, hardly anyone was talking about it. Today there are countless articles, webinars and reports about how AI will “revolutionize” the mortgage process.

Which we find a little odd, since AI is not new at all—not even to the mortgage industry. But then, what passes for AI these days is not often what it’s cracked up to be.

AI is More Than Templates

Over the past several years, countless mortgage software providers and even a growing number of mortgage lenders and servicers have been boasting about how they are using AI to reduce or eliminate manual work, particularly when it comes to processing and auditing loan files. In reality, however, AI has been used for loan document processing and data extraction for more than 30 years. How do we know? Because that’s how long we’ve been doing it.

Yet these days, almost every document processing software provider claims its products use AI. The reality is that most of our competitors’ products do not really use “intelligence” to classify documents. Instead, they identify loan documents by comparing it to documents with the same template. This is great if you’re using commonly used documents, such as 1003 and 4506-T forms. The problem is there are many variances in the types of loan documents most lenders and servicers come across.

As a result, most document processing software is unable to classify a document that is unfamiliar or has an unstructured layout. Without a template, it has no way to identify it, let alone extract meaningful data from it. For this reason, most of these products aren’t really “intelligent.” They can only make decisions based on the documents they already know.

How Real AI Goes Deeper

Like other providers, Paradatec is capable of identifying common loan documents based on templates. But unlike other providers, our technology can identify virtually any document, whether it has seen the document before or not.

That’s because we combined our proprietary optical character recognition (OCR) technology with natural language processing technology, which uses AI-based algorithms to “read” entire documents. By doing so, it can also find and extract data on virtually any document, even if the document layout varies significantly from other documents of the same type.

This is particularly useful for unique documents, like deeds, liens, notes and addendums. Take warranty deeds, for instance. There are many different variations of what a warranty deed looks like. However, almost every warranty deed contains the same information. By looking at the OCR output, our technology can tell it’s a warranty deed because it has learned what sort of information warranty deeds contained.

Not Just Better—Faster, Too

By the way, you might think that a technology that reads entire pages of documents at a time would be much slower than software that relies on templates. But that’s not the case. Our technology can process roughly 3,600 pages per hour, which is at last five times faster than any other product on the market today. In fact, a top three sub-servicer used our technology to ingest 25,000 loans in four days—while simultaneously fully re-indexing and restacking every loan, extracting key data elements and comparing that data to the prior servicer’s database.

That’s the true power of AI. So, the next time you hear someone talk about AI, you’ll know whether they’re truly intelligent about the subject—or maybe being a little artificial.

If you’d like to learn more about how Paradatec can transform how you manage loan documents, drop us a note at We are happy to tell you more.

Our Clients Love Us

From originators to servicers, BPOs and external due diligence firms trust Paradatec to streamline document processing.

We asked a number of vendors, including the Paradatec team, to help us perform an extensive due diligence process that included an out-of-the-box, blind test with our own loan samples and proof of concept test.  Paradatec was the clear winner based on our comprehensive vetting process.

Steven Davids
Senior Vice President of Correspondent Lending, Northpointe Bank