Welcome to the new subscribers who joined our mortgage tech newsletter last week.
Today, let’s talk about operational mortgage automation.
Everyone has some sort of understanding of what automation is.
But automation is such a loaded term that it is often too abstract to be useful.
In this issue, I’d love to share a more practical overview of operational mortgage automation, how it works, and its applications.
I hope this will help you identify more opportunities to apply it in your mortgage operations.
Below, you can find my analysis of:
Let's start by unpacking this loaded term.
Automation refers to using software to perform tasks that would otherwise require human intervention.
Operational automation refers to using software to automate routine and repetitive tasks in business operations.
Operational mortgage automation refers to applying automation specifically to the operational aspects of mortgage lending.
It focuses on operational processes like loan processing, underwriting, servicing, and default management.
The goal is to increase efficiency, reduce human error, save time, and lower costs while ensuring compliance with regulatory standards.
There are many tech and software systems used in mortgage operations that might seem like operational automation but aren't.
For clarity, let's define what operational automation is not.
Operational automation is about performing actions.
Thus, if software doesn't actively reduce the actions a human needs to take, it's not considered operational automation.
For example, a document storage system like Dropbox or an analytics dashboard isn't operational automation.
I even lean towards categorizing AI products that help extract data from documents into operational data engineering rather than operational automation. It definitely can be a step within automation. But until the software system takes any actions based on this data, it is more data transformation software.
Operational Mortgage Automation is closely intertwined with operational data, analytics, and apps. Understanding how they relate to each other might help you in your mortgage automation efforts.
Data is the foundation of automation. Without operational data, there is no operational automation.
Thus, most automation projects start by first collecting the required operational data.
Usually, changes in operational data trigger automation processes. Automation then utilizes this operational data to perform actions.
Like automation, operational analytics relies on the same operational data. But unlike automation, the primary function of analytics is to answer questions based on the data. While for automation, it is to perform action based on the data.
Analytics and automation go hand in hand in the improvement of processes.
Here are 2 use cases of operational analytics in the context of automation:
The terms automation and software applications are often used interchangeably. While automation is part of most applications, they aren’t the same.
Applications like automation and analytics rely on the same operational data to function.
But the primary function of the applications is to:
Most automations are invisible to us. They work under the hood, triggered by changes in operational data, take actions via APIs, and store output in applications.
Applications serve as a user interface to interact with automation and operational data.
A few traits in operational mortgage automation make it highly practical in mortgage operations.
While I don't think there is a rigid line between operational and traditional automation, contrasting them can highlight these traits more clearly.
Here's my analysis of the differences between the two.
Operational mortgage automation focuses explicitly on the operational aspects of mortgage lending. It deals with automating actions in processes like loan origination, underwriting, servicing, and default management.
Traditional mortgage automation focuses on broader aspects of mortgage lending, including marketing, sales, and customer support.
Operational mortgage automation is process-oriented, focusing on automating multiple actions that compose an entire process, allowing data to flow from one step to another.
Traditional mortgage automation, however, tends to automate narrow, isolated tasks within a process.
Operational mortgage automation relies on the operational data automatically pulled from various systems like LOS, CRM, data vendors, etc.
Traditional mortgage automation relies on manually inputting data into the system by humans.
Operational Mortgage Automation is triggered in response to real-time changes in operational data, operating without manual intervention.
Traditional Mortgage Automation relies more on a human initiating the automation. Automation may carry out tasks autonomously, but it only starts when a person activates it.
An automation system consists of a set of automated workflows.
These automated workflows can be distilled down to four key concepts:
A workflow consists of a trigger and one or more multiple steps. Each Step represents an action that needs to be taken. Events in operational data trigger workflows. Workflows generate a Run for every time it is triggered. A Run is a single execution of the Workflow.
Below is a deeper overview of each concept.
Triggers define WHEN a workflow should be run.
A Trigger acts like a sensor, monitoring changes in operational data and initiating an automated workflow when specific conditions are met.
Triggers can be categorized into:
Here are a few examples of the triggers:
Triggers ensure that workflows are executed when they are supposed to, including in response to real-time changes in operational data.
Steps define WHAT actions should be taken when a Trigger initiates a workflow.
Think of steps as the blueprint for the workflow. A workflow usually consists of multiple steps, although it can sometimes be a single step.
Each step is defined by:
Here’s an example of the steps in a workflow:
“1.” defines the order of the action, “( )” is an action, and “[ ]” is a parameter of the action.
A workflow can consist of multiple steps, executing the same action with different parameters, like the first 3 steps in the example above.
Actions define what staff your automation CAN DO. It is like the skills of your automation system.
Typically, actions involve running computations on the supplied data or interacting with other software systems via APIs.
Here are some examples of actions an automation system might perform:
An action is the component that generates the core value of an automated workflow.
The more actions your automated systems can do, the more processes you can automate.
Each automated action is one less action that needs to be taken by a human.
A Run is a single execution of the workflow.
Anytime your workflow runs, the software system executes each workflow step in order.
The workflow run ends when all the tasks have been executed, and any final outputs have been produced.
Single Run is usually defined by the following:
The primary function of automation in mortgage operations is to perform actions that would otherwise require human involvement.
So, what defines how operational automation can be applied to mortgage operations is WHAT ACTIONS automation can get done.
Below is a list of common actions that can be automated in the mortgage process. The list is not exhaustive but should give you an idea of what's possible.
As technology advances, the range of actions that can be automated expands, especially with the latest developments in AI technology. More and more tasks that traditionally required human intervention can now be effectively handled by software.
Given enough data, most routine actions taken within software applications can be automated through API or RPA.
Here are some examples of actions within this use case:
Operational automation can generate real-time or near-real-time alerts based on operational data. These alerts can trigger the sending of SMS, emails, or messages through various messaging platforms (such as Slack, Teams, WhatsApp, etc.).
Here are some examples:
Typically, alerts fall into one of the following categories:
In the same way actions and data entry are automated within software services, actions and data entry can be automated with the vendors through API or RPA.
Here is a list of orders that can be automated:
A significant portion of the documents traditionally created manually during the loan lifecycle can be automatically generated. This process involves using document templates and populating them with the necessary data.
Here are some examples of documents that can be automated:
AI and ML technologies are extensively utilized for fraud detection automation in the mortgage industry. These technologies enable software products to identify fraud that might be invisible to the human eye.
Below are a few types of fraud detection that can be automated:
Risk assessment and decision-making tasks in mortgage operations can also be automated.
Here are some applications of automation in the risk assessment and decision-making process:
Thanks to advances in the OCR and AI, a great deal of the document analysis and validation can be automated.
Here are a few examples of how document analysis and validation can be automated:
Using AI to classify documents and APIs of the file storage software, you can automate document indexing and storage tasks.
Here are a few examples of how these tasks can be automated:
Automated systems can do automatic calculations relying on the operational data.
Here are a few common calculations in the mortgage process that can be automated:
Automated systems have made it possible to programmatically move money between bank accounts.
Here are some use cases within mortgage operations:
The high-level process of developing operational mortgage automation involves these steps:
Here are some key tech used in operational automation:
An in-depth article on building operational mortgage automation is coming soon.
I hope this post gave you insight into Operational Mortgage Automation and how it can be used in mortgage operations.
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