Mortgage is one of the few industries where blowing the due date can cost a company a 5-figure loss.
So, ensuring that nothing falls behind is a crucial part of efficient mortgage operations.
In this issue, I’ll share how Operational Analytics can be applied to mortgage operations to automate the due-date tracking process.
This issue consists of the 3 parts:
I hope it will give you insight into how you can use Operational Analytics to automate the due date tracking process and gauge if it can benefit your mortgage operations.
Before trying to automate any process, it’s a good idea to clarify:
Below, you can find my analysis to answer these questions.
Mortgage lenders have quite a few due dates to meet throughout the loan origination lifecycle.
These dues dates usually come from:
Here are a few examples of the dates:
And for each due date, the process of tracking comes down to:
Because there are more due dates than a single person can remember.
And when mortgage lenders miss due dates, bad things happen.
So missed due dates lead to:
From my research, the old way of staying on top of due dates looks like this:
Individual team members often have their own way of tracking due dates in addition to the one set by the company.
The solution's most significant limitation is that it relies on manual entry.
As a result, it is highly prone to human error:
The odds of human error might not be significant enough to bother about them at a smaller volume.
But the more loans in the pipeline, the higher the chances of something slipping through the cracks.
I don’t see direct costs associated with the current solution as it relies on existing tools like LOS or free ones like Google Calendar and Spreadsheets.
But some indirect costs come from the manual nature of the solution:
Operational analytics is a branch of business analytics that focuses on the optimization of the day-to-day operations of the company.
Traditional analytics is about analyzing a broader set of historical data to understand past performance and plan future strategies. While operational analytics is about using real-time data for optimization of current operations.
Operational mortgage analytics focuses explicitly on the optimization of mortgage company operations.
Operational mortgage analytics consists of 3 building blocks:
Real-time data is the foundation for Reporting and Automation.
Usually, the data for operational mortgage analytics is in the shape of:
Data is the result of the Data creation process.
Data creation is the process of gathering data from existing sources like LOS, POS, etc.
Then, it is cleaned, enriched, and transformed to be usable for Reporting and Automation.
Reporting enables you to get answers to questions such as:
And it lets you get them fast.
Reporting is the result of the Data visualization.
Data visualization is the process of creating a visual representation of data.
Automation lets you operationalize processes, reduce human error, and free up the team’s time.
A few examples of Automation:
Automation is the result of the Data Activation.
Data activation is the process of Automation of actions based on real-time data.
With operational analytics, you can automatically calculate due dates and capture them based on the changes in your LOS or other system.
It relies on automated future activity capture, where, based on the real-time loan activity, you generate new activities that should happen in the future.
For example, you can capture due dates (future activities) based on the real-time loan activity like:
And then automate the calculation of their due date based on the:
Automated future activity capture can reliably generate a feed of the due dates that you can later use for reporting and automation.
You can set up reports based on the auto-generated feed of the due dates (future activities).
These reports can give you a quick overview of where each loan stands in the pipeline and if any action is required.
For instance, you can use these reports to answer questions like:
You can visualize your due-dates reports as a timeline or table for easier comprehension. Here are a few examples below.
Table view (example 1):
Table view (example 2):
You can use a combination of filters like the ones below to get as granular as you need:
Due date type (activity type)
How far into the future
You can configure automated EMAIL or SMS notifications about approaching due dates based on the auto-generated feed of future activities.
It relies on the concept of the alerts that can be triggered when certain conditions are met, for example:
You can even trigger alerts based on the days since activity has happened to send alerts for things without set-in-stone due dates but need timely action.
For example, trigger an alert when a Loan was funded but wasn’t sold within 7 days.
Once alert triggers are configured, they listen to the live event feed, and when conditions are met, they trigger an alert.
Then, you can use alerts to trigger automation that sends SMS or EMAIL using any workflow automation tool like Zapier.
Since due dates are automatically captured and reconciled, you have a single place with always up-to-date information about due dates.
So even if someone is OOO, you still can easily retrieve their due dates and continue work without disruptions.
I hope this post gave you insight into how Operational Analytics can be used to automate due-date tracking in mortgage operations.
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