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Investments Lending Servicing

[Part two] Ellie Mae: We need to rethink the effectiveness of the Disclosure Desk

The true cost of the disclosure desk

August 12, 2016

This is part 2. Check here for the first part to this blog. 

The True Cost of the Disclosure Desk

Among our survey group, the monthly cost of a fully staffed disclosure desk ranged from $30,240-$70,560 with mean of $44,352. At that amount, a 60% waste rate equates to an average of $26,611 in monthly costs, or $319,932 annually.

Also factor in the number of cures still needed by this supposedly thorough, clearly inefficient process. By eliminating the 60% inefficiency rate, a lender could not only improve the efficacy of generating disclosures but also drive down monthly cure payments through improved quality of the data within the delivered disclosures.

The lenders in our survey incur an average of $35,800 in cure costs per month.  This could be significantly reduced by improving the process.

Let’s assume that an improved process increased accuracy 25% through automatic data validation, technology to interface systems, and complex business rules. The expected annual savings would total $100,000. The waste of the average disclosure desk then could be $419,000 or more in any given year between inefficiencies and ineffective review.

Eureka! Finding the gold  

Uncovering the ineffectiveness of your disclosure desk requires examinations of each individual step and asking “what, why, and how?” Why is that step being performed?  What is the cost of that step? What value does that step add to the process?  Why is the reviewer taking that particular action?  How can that step be error-proofed in order to be eliminated?

Working in conjunction with a team of business analysts and development engineers, Ellie Mae has created the two high-level guidelines for making the disclosure process an effective and quality-driven component in the life of the loan.

1. Error-proof the process

Many of the tasks performed by disclosure desk today are those of inspection. Up to 35% of the individual action items involve verifying the presence and/or the accuracy of data entered by the previous supplier in the supply chain (Loan Officer, Processor, etc.).

Implementing Business Rules around the entry of data and eliminating the possibility of inaccurate data where possible are the two most impactful steps necessary to create an error-proof system.

Here are specific examples of rules that can be written within a loan origination system to help demonstrate quality and completeness on the loan file.

  • Require field data entry throughout the workflow. Do not allow the user to “skip over” fields that impact the accurate completeness of the loan file. Enforce hard stops during critical workflow stages; especially the disclosing event. Make this as dynamic as to not create unnecessary noise on the workflow (e.g. MI is required on conventional loans over 80% LTV)
  • Enforce range limitations on fee fields. Disallow field values that clearly would not “fit” the loan scenario
  • Auto-populate fields that can be derived across forms. There are some common fields that can be auto-populated based on data found in related fields
  • Variable fees should be automated as best as possible. Consider using third-party vendors to ensure accuracy. Review processes that cause review or are prone to error. If a lender’s credit application is not working for your process, put it in a box and have a computer make the decisions
  • Create a process for better management of documents for eDelivery (eliminate need for adding and dropping disclosure documents on the fly).
  • Look at common systems that you use for verification and see if APIs are available. If so, integrate as much as you can to achieve a seamless process.
  • Centralize information for quick understanding of loan details. Stop reviewing every loan. Use dashboards to create on-screen color-coded visual aids for quickly determining the quality of the disclosure package. Let an “expert” typically assigned to the disclosure desk scan the dashboards to address those loans that do not pass scrutiny.
  • RED- full review
  • YELLOW – review highlighted items
  • GREEN – NO review required

2. Emphasize a culture of training and quality

  • Let those with the borrower relationship drive the process.
    • This supports the eSign process by coaching borrowers through the process of eConsent and eDisclosure process
      • Borrower relationship
        • Better customer retention (pull-through to funding)
        • Improved customer service
        • Associates the action to those responsible for customer relationship
      • Closer to the act of change request
        • Eliminate any disconnect between change request and action to be taken.
        • Better custom relationship – coach the borrower through the change implementation i.e. borrower cost
        • Originating loans is a continued sale, not just selling the borrower initially.
        • No surprises at closing
      • Create a culture of quality, starting with executive leadership.
        • Hire qualified staff 
        • Offer continuing education
        • Provide continuous coaching
      • Enforce accountability through monetary and other rewards.

While the disclosure desk itself has grown out of the necessity for quality, it has proven to be ineffective and inefficient in its evolution. Leveraging technology and a cultural shift to accommodate a more impactful disclosure process will not only provide internal efficiencies thusly reducing costs and creating opportunities for higher volumes without introducing higher personnel costs, but also produce higher quality loans with shorter run times.

Leveraging rules and automating validations without user intervention will put the process on the path of maximum efficiency. By implementing some of the basic suggestions above, the disclosure desk function could easily morph into a “spot check” based on dashboard and “traffic light” indicators alerting for inconsistencies and/or errors within the loan file.

As part of becoming a quality-driven culture training, expectation setting, and constant measuring with rewards need to become part of the process of moving from an inefficient and low-quality state to a loan process that constantly delivers quality experiences efficiently.

Simply put, there’s Gold to be found in measuring the current process for efficiency and effectiveness – constantly work toward enhancing and error-proofing – and be certain to measure the results and reward the folks performing the work.

And finally, never stop improving.  Constant improvement must be part of the process. There’s always more gold that could be unearthed. 

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