It’s not a surprise that mortgage companies large and small struggle to scrape a profit from processing loans. According to Mortgage Bankers Association, “independent mortgage banks and mortgage subsidiaries of chartered banks made an average profit of $367 on each loan they originated in 2018, down from $711 per loan in 2017.” Steve Butler of AI Foundry revealed “the cost of manufacturing a loan is the highest it’s ever been-nearly $9,000.” 

With an average cost of $2,400 on labour alone and $2,600 on back office costs to process on average 900 pages in a loan file, it’s perhaps not surprising many mortgage companies are feeling the pinch. MBA notes loan productivity dropped again from 1.9 loan per month, per employee in 2017 to 1.8 loans. Automation is the key to lowering costs.  Compliance laws, ever expanding rules and regulations make it difficult to just stay in business. Since the nature of the business has changed, it might be time to change the way you do business. Not only can this help maximise profits from current loans, but it can also help predict future profitability.

Loan Origination: Using an automation solution to verify pre-loan data and documents goes a long way to moving the process forward and ensuring accuracy. Not only can you easily verify the information on the loan documents, but you can also use automation like RPA to gather data from external websites, portals and legacy systems. RPA is also used to create and enforce standardised workflows, providing checks and balances.

Loan Application: First you need to identify, then classify each document. Manual identification and classification take time to visually identify and confirm that every document and data point has been received. You also need to check that there are no additional queries or paperwork needed. For many mortgage organisations, a single loan application can take hours to complete. And depending on the time of year, high volumes of loans require additional hours and head count to keep up with demand.

Document Submission and Verification: From W-2s, pay stubs and various bank records, these documents need to be digitised and classified into data points that can be used to complete the loan package. People who are self-employed need to provide historical P&L statements, and depending on how that information is provided, it can be challenging to pull the needed data points into the LOS. Instead of having employees manually review, cognitive automation recognises and learns the data coming in, migrating the data automatically into the LOS. Only when there’s missing data or an error is human involvement required.

Underwriting: Typically, you have a case worker who compiles all the information needed for a loan document, such as applications, title searches, appraisals, mortgage lender approvals, insurance, and soon. By implementing an integrated automation platform solution, underwriting and dispersing loans moves much faster. For example, this process could take up to 45 days, but with automation you could cut that by around one-third. Automation provides productivity and accuracy improvements through the lifecycle of the loan. The loan now closes faster, and your team can move to processing other loans. And customer experienced is enhanced because the process is much easier and quicker than expected.

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