How automation can take the pain and risk out of mortgage post-closing

Processing problems can create an existential crisis to lenders. A recent study by Freddie Mac found that the top two causes of loan quality defects are missing documentation and miscalculation of income due to missing documents. In a loan defect scenario, the lender is typically on the hook to fix the problem, a situation that can be disastrous if the solution requires repurchasing a loan.

Conversely, changes brought about by the forced isolation imposed by COVID-19 have enabled automation to be implied in new ways. For example, the number of title and settlement companies that offer digital closings jumped from 14% in 2019 to 46% two years later while the adoption of remote online notarization soared 547% in 2020 alone.

That means there's never been a better time for lenders to investigate automated solutions to all aspects of loan processing, including mortgage post-closing.

Details, details

The mortgage post-closing process is excruciatingly detailed. It involves thoroughly reviewing and verifying all documentation that was created during loan issuance, including verification of the borrower's income, employment, and credit history, as well as the property's value and condition. Verifying that these details have been considered reduces the risk of nasty surprises later.

Post-closing also requires that the loan-to-value (LTV) ratio be verified. That's a loan quality formula that determines whether the terms of the mortgage are within acceptable limits and that the borrower has the ability to make payments. Requiring private mortgage insurance or a higher down payment from borrowers who have a higher LTV helps mitigate the risk of default in the event of a market downturn or other negative events.

Escrow fees must also be reviewed to ensure that borrowers have sufficient funds to pay for property taxes and insurance without incurring large shortages that the lender must cover temporarily. 

All these steps involve complex financial calculations that in most cases can be automated to significantly reduce the risk of error. Automated solutions apply formulas that reflect best practices in the industry and evaluate the terms of a loan against industry averages to spot high-risk candidates.

Lenders can further reduce risk by monitoring the loan's performance after closing. That includes regularly checking the borrower's credit score, monitoring the property value, and ensuring that the borrower is making timely payments. Automated checks can reduce labor costs and the risk of error by monitoring accounts for anomalies such as repeated missed payments and changes in a borrower's credit score so that steps can be taken to prevent default.

Many aspects of the ongoing service of a loan can also be automated, such as validating payments, maintaining account health, flagging exceptions, and generating records. Automated monitoring and tracking ensure that borrowers make timely payments. Specialized compliance software ensures that loans meet legal and regulatory standards to reduce the risk of fines and penalties. 

By automating the process of collecting and analyzing data, lenders can apply modifications specific to the borrower's financial situation. That not only saves time and cost but enhances borrower satisfaction.

AI game-changer

Artificial intelligence has generated intense interest in the mortgage industry because of the technology's potential to automate decisions that currently require expensive human judgment as well as to attend to details that people easily overlook.

For example, errors are a major cause of delays in loan processing. The average error rate for manual data entry can range from 1% to 5%, but that figure can be higher if the tax is complex or the data is confusing. Error rates are even higher for tasks that require complex decision-making or interpretation. In comparison, automated data entry technologies like robotic process automation (RPA) are as low as 0.1%. 

AI-powered underwriting systems can quickly and accurately assess a borrower's creditworthiness, reducing the risk of errors and minimizing the likelihood of default. Lenders can use machine learning to pore over millions of credit records and identify high-risk borrowers according to factors that a human operator wouldn't even consider.

AI can monitor loan performance and detect early warning signs of default, such as a change in spending patterns. Machine learning can even be applied to recommend proactive measures to address issues before they become serious problems.

In the paper- and human-intensive process of loan modification, AI can automate the collection and analysis of data within regulatory guidelines and can recommend a plan that's customized to the borrower.

Applying technology to the post-mortgage closing process needn't be a "big bang" scenario. For example, RPA is a type of software that can be programmed to imitate routine tasks such as transposing figures from a document to a spreadsheet. It's fast to implement, relatively inexpensive, and is appropriate for many labor-intensive functions such as verifying the completion of checklist items or even post-closing audits. That reduces errors and frees up employees to work on more challenging tasks. Fannie Mae and Freddie Mac, which regulate most mortgage loans in the United States, both recommend the use of RPA.

Don't overlook automation's collateral influence on employee morale, an issue that has become more urgent during the post-COVID-19 "great resignation." Some 85% of executives who responded to a 2021 International Data Corp. said that improved employee experience and higher employee engagement translate into better customer experience and higher revenues. Technologies that offload mundane tasks from employees contribute to improved job satisfaction, lower turnover, and higher productivity.

The mortgage post-closing process will never be entirely risk-free, but the judicious use of automation can make a complex and error-prone process a little easier on everyone.

For more information on Mortgage Post-Closing and Information Lifecycle Management, explore our Mortgage and Loan solutions.