For years, banks and financial services firms have been trying to automate document-intensive workflows such as the mortgage origination process, but with limited success.

Early financial services process automation attempts relied on systems based on keywords, rules, or templates, all of which fall short when confronted with unstructured data. Such approaches would have to account for every possible document type a financial institution comes across – an unlikely scenario given the range of documents and data involved in many processes.

Consider customer onboarding. It requires all sorts of documents to get a new customer set up correctly, including the bank’s application forms, perhaps tax returns, statements from other institutions, credit reports, and the like. You would have to spend vast sums paying consultants to develop templates or writing rules for every possible document type you may encounter. Even so, as soon as a new document type comes along or an existing document format changed, the automation breaks down. Money wasted.

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OCR is machine learning technology that can convert documents such as PDFs into a machine-readable format. That’s useful, but it still leaves you with templates to extract useful information and issues when there’s a document change.

RPA can handle automating processes that involve the exact same steps each time. Say, for instance, a bank data entry clerk enters the same exact keystrokes in the same order time after time into a mortgage processing system. That would be a process ripe for automation using RPA.

But that’s not at all how processes tend to work and especially not when you’re dealing with unstructured data, which is part of most documents to be processed. With unstructured data, a human has to read the document and decide what data to extract. (RPA can, however, complement Intelligent Automation in a financial services automation solution, as we discuss briefly below and in detail here.)

Banks, mortgage brokers and others have been processing record numbers of mortgages over the past several years, which would normally be considered a good thing. The bad news is nimble FinTech startups and non-depository loan originators are using automation to process mortgages much faster than their traditional rivals.

Overall customer satisfaction with primary mortgage originators dropped five points (on a 1,000-point scale) in 2021, driven largely by declines in satisfaction with the refinancing process, according to a study by J.D. Power.

To turn it around, banks and mortgage brokers will have to adopt mortgage automation software like those of their FinTech competitors, which use chiefly online tools throughout the mortgage process. Indeed, Rocket Mortgage, a perennial favorite of the likes of Money magazine, is available in all 50 states but offers no in-person service.

Intelligent document processing provides a solution. IDP can address numerous steps in the mortgage process, including by:

• Automating mortgage application processing
• Data collection and classification automation
• Streamlining mortgage contract processing
• Applying artificial intelligence to data extraction
• Speeding prequalified mortgage processing
• Automating mortgage underwriting
• Providing loan closing support

An intelligent document processing platform can help you greatly increase the speed as well as the accuracy of myriad processes involved in mortgage processing, including the following:

Accurate mortgage document classification

One of the first steps in the mortgage process is simply accepting and classifying all the documents that come in from applicants. Typically (but not always) they now arrive via an online portal. But someone must still look the documents over and categorize them according to type, whether it’s property specifications, purchase & sale agreements, W-2s, pay stubs, identification (i.e., passport or driver’s license), real estate bills, insurance forms, and the like. Only then can you send them along to the next appropriate stop for further processing. It’s a process that takes hours for humans but mere minutes for a properly trained intelligent document processing model.

Mortgage data extraction

Once all the documents are collected and categorized, the next step is to extract relevant data from them. Here again, this requires an employee to read all the documents to identify pertinent information, then cut and paste it into the downstream LOS or other decision support systems. It’s another time-consuming, rather mundane process. Monotony often leads to errors and certainly to employees who find their work less than rewarding. With IDP, those same employees train models to identify the relevant data to extract and the model handles if from there on, leaving employees free to take on more satisfying and valuable work.

Automate Mortgage Prequalification: With appropriate data in hand, an IDP platform can automate the process of pre-qualifying applicants by applying their FICO scores. Those with the best scores may be sent on a fast-track route for expedited processing. Fair scores may be sent for further analysis by credit experts while those with poor scores are automatically sent a rejection letter. In this fashion, the bulk of underwriting analysts’ time is spent only on those applications that most require their expertise.

Credit underwriting, loan closing

The underwriting process can also be a highly manual one in some institutions, involving preparing and collating documents for analysis, further adding to processing time. This also extends to the closing process, which is often still rife with paper that customers must sign to complete the loan process. Generating all those documents, of course, typically requires manual intervention, making it another candidate for intelligent document processing.

Automation in the mortgage industry can help you break free of these laborious, paper-intensive processes and compete effectively with FinTech’s. To learn more, check out our e-book, “Unlocking the Value of Unstructured Data for Financial Services.”

In this age of digital transformation, financial institutions and banks must take steps to achieve efficiencies wherever they can. Our intelligent document processing solution provides document automation for financial services delivers significant benefits, including:

A common misconception about automation, it will displace lots of humans from their jobs. In our experience, that is not the case. Instead, automation augments employee’s work, relieving them of the most repetitive, uninteresting aspects of their jobs so they can focus on more important matters.

A recent report by the McKinsey Global Institute supports this notion. It said only about 5 percent of occupations “could be fully automated by currently demonstrated technologies.” According to McKinsey, the more likely scenario is that portions of jobs will be automated – about 30 percent of the activities in 60 percent of all occupations,

In the financial and banking world, this means your employees will have more time to dedicate to strategic efforts that give you a competitive edge.

What Indico calls intelligent process automation can go by various other names. Analyst firms, including the Everest Group, use the term “intelligent document processing” while others go with just “intelligent automation.”

Gartner lumps various automation technologies under the term “hyperautomation,” defined as: “Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.”