CASE STUDY

Asset Management/Loan Servicing: A SOLUTION TO DEFAULT REPORTING

www.accelirate.com

Accelerating Automation & AI

ASSET MANAGEMENT/LOAN SERVICING: A SOLUTION TO DEFAULT REPORTING

2

EXECUTIVE SUMMARY

The client is a mortgage investment firm that currently has over $10 billion in assets under management along with loan servicing and origination affiliates. In 2017, the client embarked on a journey to transform the organization’s approach to Enterprise Optimization by utilizing a multitude of technologies including Robotic Process Automation, OCR and Process Re-engineering methodologies.

After establishing our knowledge in automation and performing a Proof of Concept, the client chose to partner with Accelirate to establish its Robotic Process Automation capability within the organization.

ASSET MANAGEMENT/LOAN SERVICING: A SOLUTION TO DEFAULT REPORTING

3

SCENARIO

Within the client’s many different departments in the Loan Servicing division lies a Government Loan Servicing business unit. Within this unit lies loans that are owned by a Government Sponsored Enterprise (GSE); the bulk of the population being serviced fall under the Fannie Mae/Freddie Mac umbrellas. Each individual loan that falls under these umbrellas need to have their status reported monthly based upon certain loan criteria that classifies them as either Current, Paid Off or Delinquent. Due to the sheer volume of loans involved in these pools, GSE’s will provide an ‘incentive’ for each loan being accurately reported.

CLIENT’S ISSUES & CHALLENGES

The client’s process of researching each individual loan and determining whether it was in the correct status was extremely manual and time consuming. Due to not having enough resources to accurately go through each of the tens of thousands of loans being serviced, the client was losing ~$50,000 in lost ‘incentives’ per month due to inaccurate reporting for just the Fannie Mae account and an additional ~$10,000 for Freddie Mac. Users would:

- Obtain information from a report generated weekly and filter it based on subjective parameters within this report to look for ‘outliers’ that would signal that a loan might be classified incorrectly.

- Once a set of loans had been identified, the processor would research the loan in their loan servicing systems to comb through a set of certain indicators that would indicate whether the loan is correctly classified.

- If a loan is incorrectly classified, the processor would go into a separate system of records to update the loan with the correct status.

Keep in mind, due to the volume of loans in each umbrella, the amount of resources necessary to go through each loan monthly and provide accurate reporting is extremely resource-intensive. Luckily, due to the nature of RPA and the methodology implemented by Accelirate, we were able to provide a robust automation that solved for all of the above-mentioned challenges.

The ‘As Is’ Handling Time for Each of the 3 Umbrellas

Loan Type Time Spent Per LoanCurrent Loans 2-3 Minute CyclePaid Off Loans 2-3 Minute CycleDelinquent Loans 5-7 Minute Cycle

ASSET MANAGEMENT/LOAN SERVICING: A SOLUTION TO DEFAULT REPORTING

4

ACCELIRATE’S SOLUTION

Accelirate took a holistic approach to this automation and decided instead of replicating the current process that the human was following, we would look at the overall goal of this process and how we could enhance the current state by utilizing leading RPA technologies.

We determined that over 80% of ALL VOLUME of the loans were classified under the Current & Paid Off categories, however, due to the average cycle time for each Current/Paid Off loan (~2-3 minutes) the client did not have enough resources to handle this volume. Instead we created a bot that would go through each of these data sets (via DB Queries) and if they were not correctly classified, the bot would clear out their incorrect status within their system of record and provide a report to the business.

The Delinquent Loan population, though a much smaller volume, was much more complex in its handling criteria and was the cause of the $50,000/Mo. loss in incentives to the business. By partnering with the business, we were able to create a Waterfall Matrix that would consider a logical hierarchy for each individual Delinquent Loan.

Delinquent Loans fall into one of the three categories: Loss Mitigation, Bankruptcy, Foreclosure. The Waterfall Matrix we created for the client was broken up into 5 tranches, or ‘priority levels’, that represent into which of the three categories the loan was being classified under, see below:

Within each of these priority levels are different statuses that represent one of the overlying categories, for example, one of the statuses a loan can be under in the ‘Loss Mitigation’ category can be an ‘Active Modification’ where the loan servicer is actively trying to modify the loan before it goes into Bankruptcy/Foreclosure category.

The status breakdown that needed to be accounted for each Priority Level within each of the 3 categories is as follows:

Loss Mitigation

Loss Mitigation

Bankruptcy

Bankruptcy

Foreclosure

Foreclosure

Priority Level 1Priority Level 2

Priority Level 1Priority Level 2

9 Statuses2 Statuses

13 Statuses2 Statuses

7 Statuses

Priority Level 3

Priority Level 3

Priority Level 4Priority Level 5

Priority Level 4Priority Level 5

ASSET MANAGEMENT/LOAN SERVICING: A SOLUTION TO DEFAULT REPORTING

5

Within each of these potential statuses are a set of ‘logical indicators’ that we had to clearly outline with the business that the bot must check against, providing an additional layer of complexity to this automation.

The number of indicators per Priority Level are as follows:

With a total of 42 Logical Indicators that must be accounted for, this automation proved to be complex in nature but robust in accounting for a wide variety of potential scenarios that a human counterpart would have difficulty in accurately performing for each individual loan in the delinquent population on a consistent basis.

Additionally, due to the sheer volume of loans and the handling criteria required for each delinquent loan, the business was only able to perform this process monthly on ‘outlier’ loans in their reports with clear indicators that the business could identify easily. By implementing our solution, the bot was able to apply our robust Waterfall Matrix to each loan and correctly identify the status the loan should be classified under.

Loss Mitigation

Bankruptcy

Foreclosure

Priority Level 1Priority Level 2

11 Logical Indicators4 Logical Indicators

14 Logical Indicators4 Logical Indicators

9 Logical IndicatorsPriority Level 3

Priority Level 4Priority Level 5

ASSET MANAGEMENT/LOAN SERVICING: A SOLUTION TO DEFAULT REPORTING

6

Business Value of Accelirate’s Solution

700-1,000 hours of potential man hour savings per week on the Current/Paid Off loan populations by going through each dataset on a more frequent basis than the business can currently perform due to resource availability. The current Volume is 14,000+ Current Loans (478-718 hours saved) and 6,500+ Paid Off Loans (220-331 hours saved) weekly.

1,000-1,400 hours of potential man hour savings per month on the Delinquent loan population by going through each dataset on a more frequent basis than the business can currently perform due to resource availability. The current volume is 12,000+ Delinquent loans and 1,025-1,435 hours saved per month.

Customization: The automation was configured in a way to allow for easy integration of additional requirements that are outlined by the business users.

Greater audit capabilities: Software bots are able to provide audit reports of every loan that is processed and give a ‘snapshot’ of what status the loan is currently classified under and if a change is needed, provide a log of what new status it will be classified under.

Increased reliability and consistent reporting of each loan in the Current, Paid Off and Delinquent populations.

Reduced $50,000 loss of incentives per month on the Delinquent loan population by applying a Waterfall Matrix model that can classify a loan into one of 33 potential statuses. Any loan that didn’t fall into of these statuses were classified as an exception, provided to the business and if their indicators could be mapped, were included in future development phases to increase population handling by the bot.

Greater employee satisfaction by eliminating the need to go through a large sample of loans monthly and instead only needing to focus on those that cannot be classified in the Waterfall Matrix.

Accelirate Inc1580 Sawgrass Corporate ExpresswaySuite 110Sunrise, FL 33323Info@accelirate.com