3 Ways Intelligent Automation Transforms the Mortgage Origination Process

January 4, 2021 / Commercial Banking, Financial Services, Intelligent Process Automation, Use Case

How Automation streamlines mortgage origination

Banks and mortgage brokers have a problem: their customers aren’t too happy with them and one big reason is their mortgage origination processes are too slow, a situation that is crying out for intelligent automation.

Banks are losing business to non-depository loan originators, which have doubled their market share from 25% to 50% over the last 10 years. Fewer than half of bank customers were satisfied with their bank for both original mortgages and refinances, trailing their non-bank counterparts by 20% to 30%, according to a survey by McKinsey & Company.

Among the key criteria customers care about is speed. “Customers move fast and expect institutions to be nimble enough to keep up with them,” McKinsey found. “They want to complete the application quickly and, if they already have a relationship with the lender, they expect the lender to use the financial data it already has rather than ask them for more documents. Naturally, borrowers also want a quick conditional decision and fast time to close.”

For banks that have highly manual processes, meeting those criteria will be a tall order. Yet that’s exactly what many banks have, while newer digital mortgage players are highly automated.

Banks can catch up by implementing intelligent document processing for mortgage origination, which will automate many steps that today are highly manual. Let’s look at a few of them, and how intelligent automation can help.

Customer data collection

A mortgage origination process starts with collecting data from the customer, such as property specifications, W-2s, paystubs and identification (i.e.: passport or driver’s license). For a refinance, you’ll also need real estate bills, insurance forms and more.

While most banks these days can collect such data using online portals, making it easier for the customer, the slowdown comes on the lender’s end. Reading all of these documents and pulling out pertinent information for input into lending decisioning systems is historically a manual process.

Related Article: 3 Use Cases for Intelligent Document Processing in Commercial Banking

Assessing creditworthiness

With customer data in hand, the bank must then create financial spreads from which it can assess the applicant’s creditworthiness. This involves taking data from the applicant’s various financial statements and entering them into the spreadsheet instrument.

Here again, this is often a highly manual process because, historically, automation tools weren’t up to the task of identifying the specific data that needed to be extracted from each of the applicant’s financial documents; it required human intervention.

Credit underwriting, loan closing

Once the bank assembles an applicant’s data in the appropriate format, it then goes to underwriting for a decision on whether the loan should be granted. This can also be a highly manual process, according to Moody’s Analytics. “For many lenders, the credit application represents another manual exercise in preparing and collating several separate, yet related, pieces of paper, often in a highly prescribed fashion, adding to the processing time for approval, especially for a new relationship,” Moody’s says.

The final step in the mortgage process, at least from the customer’s perspective, is the closing, where the customer meets with a bank representative (and/or lawyer) to sign the various documents and make the loan official. Before that happens, of course, all those documents have to be generated, which can again require manual intervention.

Bringing hyperautomation to mortgage processing

Clearly, this is a process that is ripe for automation. While RPA and templated approaches may offer some rudimentary help, banks quickly find they are insufficient to deal with the unstructured content in some documents and the sheer variation among documents.

A step up in what Gartner calls the “hyperautomation” hierarchy is applying artificial intelligence to the process. In this case, that takes the form of intelligent document processing, which uses AI technology such as machine learning and transfer learning to drive tools that can “read” documents much like a human would and extract relevant information for input into downstream mortgage processing tools.

Using such intelligent automation tools can also set up banks to make effective use of applications that help with automated decisioning in the approval process. Such apps typically require data to be in a pre-defined format – exactly what intelligent document processing can deliver.

Similarly, once one step in the process is complete – say, a loan is approved – that can kick off additional processes, such as the generation of all the documents needed for closing.

Slash process cycle times by 85%

For banks to compete effectively with tech-savvy non-depository loan originators, they’ll have to step up their automation game. Tools such as Indico’s Intelligent Process Automation platform can help them do just that. Indico’s platform is based on a huge database of labeled data points which, along with natural language processing capabilities, enables it to read most any document and understand the context behind it. It takes just a few hundred documents to train models to automate the mortgage origination process, or at least many of the steps.

It’s not uncommon for Indico customers to see process cycle times slashed by 85% – the kind of gains that can help you compete more effectively in mortgage processing.

The best way to understand what the Indico IPA platform can do is to see it in action, so we encourage you to arrange a free demo. Or, if you have any questions, feel free contact us. You’ll be glad you did.

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