Rocket Mortgage

Rocket Learning Center AI Chatbot

Rocket Learning Center AI Chatbot

AI Chatbot

AI Chatbot

Fin-Tech

Fin-Tech

Rocket Mortgage's Smart Liv is an experimental product: a smart chatbot integrated with GenAI. Its capabilities extend far beyond those of live agents or traditional chatbots, answering all mortgage questions and offering tools like calculators, rate checkers, and loan offer estimators.

Rocket Mortgage's Smart Liv is an experimental product: a smart chatbot integrated with GenAI. Its capabilities extend far beyond those of live agents or traditional chatbots, answering all mortgage questions and offering tools like calculators, rate checkers, and loan offer estimators.

Rocket Mortgage's Smart Liv is an experimental product: a smart chatbot integrated with GenAI. Its capabilities extend far beyond those of live agents or traditional chatbots, answering all mortgage questions and offering tools like calculators, rate checkers, and loan offer estimators.

Context

Overview

At Rocket Mortgage, I led the end-to-end UX/UI design and AI training for SmartLiv—a 24/7 virtual assistant designed to simplify home and loan research.

My Role:

UIUX Designer, AI Trainer

Teammates:

Project Manager, Product Manager, Conversational Designers, Engineers, Content Designers, UX Researchers

Timeline:

3 months: Jun 2023 - Aug 2023

Status:

0-1 Project; Launch Soon

Impact

Automate 72% non-lead generation tasks

We exceeded our product goal by automating 80% of non-lead generation tasks, resulting in a potential 44% reduction in agent labor costs.

80% User Satisfaction Rate

We achieved a 80% user satisfaction rate during usability testing, with most users expressing excitement to use our new AI Chatbot.

Company & The Team

The Company

Rocket Mortgage is the largest online mortgage company in the US.

The Team

I worked at Rocket's Conversational Design team, which creates over 60% lead generation.

The Project

Design an AI-powered chatbot for Rocket Learning Center.

Why the Project?

Why the Project?

Rocket can reduce millions (exact figure confidential) in yearly agent labor costs.

Business Goal

Business Goal

Reduce annual agent labor costs.

User Goal

User Goal

A simpler, more intuitive, and effortless home research experience.

Product Goal

Product Goal

Build an AI chatbot for Rocket Learning Center that Automates 70% of home research tasks.

Target Users

The first step was to find out who are our target users. Through research, we found that our primary users are first-time homebuyers.

75% of the Learning Center’s users are first-time home buyers.

75%

First time home buyers

25%

Repetitive home buyers

First-time home buyers typically go through a five-stage journey when buying a home, and Learning Center's users are in the early stages and using it for home and loan research.

Learning Center's First Time Home Buyers are using Learning Center for home-buying research.

Among first-time homebuyers, we identified three main user personas:

Three Personas

  1. Independent FTHB: Financially independent and prefer to research on their own.

  2. Determined FTHB: Financially stable and have already made concrete plans to purchase their first home.

  3. Aspiring FTHB: Eager to buy but not yet financially ready, seeking guidance and resources to improve their financial profile.

Problem I

Irena (Independent FTHBs) doesn’t know where or how to start the home-buying research.

Since these users are still in the early stage of considering buying a home, their research mainly focuses on understanding the whole process.

Most of them need to start learning the whole thing from scratch.

The problem here is that they don’t even know where to begin their learning journey.


I know the learning center has tons of articles, but honestly, there are just too many. I'm not sure where to start or which ones to focus on.


I know the learning center has tons of articles, but honestly, there are just too many. I'm not sure where to start or which ones to focus on.


I know the learning center has tons of articles, but honestly, there are just too many. I'm not sure where to start or which ones to focus on.

Solutions for Problem I

We designed a feature called "Offer Estimation"

Ultimately, I discovered the most practical solution: guiding users to work backward from their loan offers.

By allowing users to preview the details of their offer upfront, they can use that information to reverse-engineer the type of research they need to focus on, making their journey more efficient and goal-oriented.

Guide users to work backward from their loan offers.

User Flows for Offer Estimation

Product Goal

Step 1: Financial Assessment

The first step for users is to perform a financial assessment.

I added an “Estimate Offer” prompt on the chatbot’s homepage.

When users click through the prompt, they enter a guided conversation where they answer about 10–13 questions related to their financial profile.

How does it work?

Step 2: Estimated Offers

Once complete the questions, they'll receive a tailored estimated offer.

User Flows

Iterations for user financial assessment

How can we ask our users questions more effectively? Survey or Interactive QnA?

What I’ve shown so far is just a high-level overview of our final design. In reality, we went through multiple iterations to get there.

For example, in the initial step where users answer 10–13 questions about their financial profile, we had to figure out the best approach: Should it feel like a survey, or interactive Q&A format?

The survey allows users to make direct changes to their answers

Interactive QnA aligns better with the chatbot interaction logic

Interactive QnA

User Testing

It was difficult to choose between the two, so I conducted user testing with both options.

More users preferred the interactive Q&A. So we decided to go with it.

Iterations for offer presentation

How should we present the user’s multiple offers?

Users don’t just get one loan offer. Different loan terms and types create different amounts—so we need a clear, straightforward way to display them. For example, a 20-year FHA loan can look very different from a 30-year one, and the user should be able to easily compare them.

30-year FHA Loan

20-year FHA Loan

Proposal I

List all possible options

The first proposal was to display all possible offers to the user.

However, this approach doesn’t align with one of the key principles of conversational design—progressive disclosure.

Showing too much information at once can overwhelm the user and increase cognitive load.

Does not follow progressive disclosure

Proposal II

Add a chip for users to make selections

The second design solution was to add a chip that allowed users to select different options directly.

However, the downside of this solution is that users can only see one offer at a time, making it difficult for them to compare the differences between multiple offers at a glance.

Users are unable to make comparisons

Proposal III

Combining the first 2 proposals

We displayed two offer cards while allowing users to select different options.

Users are unable to make comparisons

Users can make a selection

Problem II

Donna (Determined FTHBs) needs to learn more detailed information to help them make a decision.

For users like Donna, their research tends to focus on more specific details to help them decide. For example, they want to know how much a house they can afford and what the most cost-effective way to purchase a home is.

Users are using calculators and rate checkers.

64%

Users are using Mortgage Calculator

64%

Users are using Mortgage Calculator

64%

Users are using Mortgage Calculator

48%

Users are using Rate Checking Tools

48%

Users are using Rate Checking Tools

48%

Users are using Rate Checking Tools

Solutions for Problem II

We integrated Mortgage Calculators directly into the chatbot.

We decided to integrate both tools directly into the chatbot’s interface. Now I’d like to showcase our approach to designing the calculators.

User Flows for Calculators

Step 1: Select a calculator

First, when users select the calculator from the left menu, they’re taken directly to a set of four primary calculators.

Here, I’d like to focus on the most commonly used one—and the one I designed—the Home Affordability Calculator.

How does it work?

Step 2. Enter the necessary data for calculation

With the Home Affordability Calculator, users enter details like the home’s address, their annual income, and their planned down payment.

The calculator then provides guidance on how much home they can afford, their estimated monthly payments, and how much of their budget remains.

User flows

How does the calculator work?

The calculator works like this: it relies on the Debt-to-Income (DTI) ratio, along with other user-provided information, to determine affordability.

DTI is a user’s total debt divided by total income, expressed as a value less than 1. The higher that ratio, the more debt the user carries relative to their income.

I initially designed the Home Affordability Calculator to use a DTI of 34% as the parameter, as it's the most recommended rate for first-time home buyers.

A little problem

However, 34% is not a One-Size-Fits-All Approach

Users can adjust the DTI by moving the slider, allowing them to see the range of home prices they can afford, along with their monthly payments and remaining budget.

Different users have different acceptable DTI ranges

30% - 40%

Most accepted DTI for May (Aspiring FTHB)

30% - 40%

Most accepted DTI for May (Aspiring FTHB)

30% - 40%

Most accepted DTI for May (Aspiring FTHB)

34% - 40%

Most accepted DTI for Donna (Determined FTHB)

34% - 40%

Most accepted DTI for Donna (Determined FTHB)

34% - 40%

Most accepted DTI for Donna (Determined FTHB)

36% - 43%

Most accepted DTI for Irena (Independent FTHB)

36% - 43%

Most accepted DTI for Irena (Independent FTHB)

36% - 43%

Most accepted DTI for Irena (Independent FTHB)

Iterations

I added a slider feature to address users' needs

Users can adjust the DTI by moving the slider, allowing them to see the range of home prices they can afford, along with their monthly payments and remaining budget.

Problem III

May (Aspiring FTHB) is working hard to find more reachable loan programs.

May’s biggest challenge is that her financial situation makes it tough to purchase a home.

She’s already tried applying for multiple loans, only to face rejection.

At this point, her main focus is identifying the most affordable and cost-effective loan programs available.

Solutions for Problem III

Loan Comparison Program

My solution for May is straightforward: provide them with a loan program comparison tool.

User Flows for Loan Comparison

Step 1: Click on the prompt to start the conversation.

I added a prompt at the conversation homepage, then we begin by asking users 3–5 brief questions to pinpoint their specific loan needs.

How does it work?

Step 2: Receive a loan program comparison

Then we present loan programs tailored to their unique financial situation.

How does it work?

Information Restructure

I worked with the content designer to restructure the loan program information.

I handled the layout while the content designer focused on the text. Together, we created a version that was much clearer and easier to understand than the original.

From user research, we found that users are primarily interested in three aspects: requirements, pros, and cons so we highlighted the three parts.

Before

After

AI Training

AI Risks

It doesn’t follow Rocket’s Voice and Tone

Our company has a specific voice and tone designed for our brand.

All of our agents are trained to use inclusive and encouraging tone when interacting with our customers.

We were concerned that the AI might not be able to maintain this standard.

It may generate inaccurate answers

While AI is intelligent, it can still make mistakes. Given that we’re a financial firm, it's critical to avoid providing incorrect answers.

What I did?

Train the chatbot with selected resources

Our Learning Center has a large collection of articles. I carefully selected the most relevant, professional, and accurate ones to train our AI chatbot.

Wrote prompts guidance to train AI

I wrote AI training prompts based on our voice and tone guidelines and established a guidance for how to create these prompts.

Establish Response Rules

Finally, we established Rejection Rules: the AI would only answer questions about financial, home-buying, and mortgages related topics.

Result

It achieved 95% AI precision

It passed out Voice & Tone test

SmartLiv accurately provided the minimum application requirements for Rocket’s FHA loan.

SmartLiv first offers an empathetic response before providing an answer