PROBLEM ⇢ SOLUTION ⇢ IMPACT

At BMO’s annual innovation sprint, we designed an AI-assisted fraud dashboard that helped analysts detect fraud 67% faster — winning Best Use of AWS.

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OVERVIEW

🧑🏻‍💻 Role
UX/UI Designer

⏰ Duration
30 hours, June 12-13, 2024

⚒️ Tools
Figma, AWS, BMO Design System

🏆 Result
Best Use of AWS Award, 67% faster fraud review

🎥 SETTING THE SCENE

Imagine Shark Tank, but BMO-branded.
At Destination Digital 2024, our cross-functional team was challenged in a 30-hour innovation sprint to design an AI-driven solution to help fraud analysts identify counterfeit cheques faster and more accurately.

THE CHALLENGE

Several tools and systems are used by fraud ops team members to determine if a check is fraudulent or legitimate, as opposed to one centralized space.

Fraud analysts were spending up to 15 minutes per cheque, manually verifying signatures, transaction histories, and account patterns.
The process was repetitive, error-prone, and stressful under time pressure, especially with thousands of daily cases.

GOAL

Allow fraud ops analysts to more quickly determine whether or not a deposited check is fraudulent or not.

Use AI to flag suspicious transactions automatically with a web page that flags identified fraudulent/valid deposits with a confidence level from 0 to 100%.

MY ROLE

🔬 Led 3 user interviews with fraud analysts to uncover workflow pain points

🖥️ Designed an interactive Figma prototype of an AI‑driven dashboard

🎨 Developed visual language leveraging BMO’s design system

📢 Created a presentation and live demo for executive judges

How did we learn more about the problem?

We had an ace up our sleeve - a team member who spent years working adjacent with the Fraud Team and knew some fraud analysts personally.
This connection allowed us access to interview 3 fraud analysts via Teams, where we could learn more about their current systems, and the problems they face.

Identifying the core issues

From speaking to the analysts, we were able to find out the following:

There are primarily two types of cheque fraud:
1. account-based/behavioral fraud (e.g. a large deposit on a newly-opened account)
2. physical cheque fraud (e.g. cheque forgery).

Manual processes involve fraud analysts reviewing one cheque, one person at a time, which takes up a lot of time (an average of 15 minutes per case).

The current technology that we have leads to a lot of false positives and missed fraud. The current software is very old and archaic.

Fraud analysts have to manually create rules based on reviewing the analytics and statistics of past and current cases which takes time.

💡 THE SOLUTION

With only 30 hours, we prioritized screens that automated manual review, focusing on analysts’ most common pain point: transaction verification.
From there, I built an AI-assisted fraud dashboard that automatically surfaced high-risk cheques using machine-learning confidence levels.
Now introducing… BMO Fraud Fighter Central!

KEY FEATURES

Home Page - a streamlined view

Fraud analysts could see the number of fraudulent transactions in a given period, graphs showing the statistics of fraudulent transactions by location and by channel, the rule efficiency score, as well as to-do items.

Cheque Review

A fraud analyst would be able to review a singular cheque, with fraud indicators calculated from 0-100 that they could review. Also, they would be able to see that person’s account and any past reviews, notes, alerts, and changes.

Rule Creation

Fraud analysts can create, edit, and delete rules that would create triggers/alerts to potential fraud. Here, we use AI machine learning to suggest rules that analysts may have missed through observation alone.

My Alerts

Fraud analysts can review fraud cases in a table view, view and assign statuses to a case, as well as escalate to a senior analyst if needed.

But how about the customer?

While this is great from the fraud analyst’s view, what if there is a way to further prevent fraud right at the source, from the customer’s vantage point?
I explored the current BMO process of depositing a cheque and introduced two more solutions to detect fraud:
1. Leveraging two-factor authentication
2. Input field verification

Two factor authentication

If a transaction seems suspicious, the app will ask the customer to authenticate themselves through a separate method (e.g. SMS, or Email) If this effort fails, the transaction will be blocked and the fraud team will be notified.

**Input Field Verification**

Users have to re-enter values taken from the cheque if it’s unclear or suspicious.

Impact

“Demonstrated a ~67% reduction in review time from 15 minutes to 5 minutes from data fed to our data model, improving case prioritization and decision accuracy; saving 200+ hours monthly.”

Quote

““The design was clean, smart, and intuitive... we can actually imagine analysts using it.””

— Destination Digital Judge

Results: We won! 🏅

I’m pictured 2nd on the right, alongside our Engineer, Product Manager, Data Specialist, and two AWS Mentors.

Our prototype won Best Use of AWS at BMO’s Destination Digital 2024. Judges praised the balance of automation and clarity, noting how seamlessly it connected to real analyst workflows.
By using dummy data fed to the data model, we found we could reduce the time spent on each fraud case from 15 minutes to 5 minutes!
This project proved that design and technology together can amplify human judgment in high-risk environments.

Conclusion

What would we redo? ⏪

We did not have a project manager on our team. If we were to redo this, I’d focus more on an execution plan for the project, as well as estimating the funding and resources needed for the project to proceed.

How would we move forward if given the opportunity? ⏩

Given more time, I would conduct usability testing with real fraud analysts to validate flows and expand the data visualization for real-time risk trends on the dashboard.

What did I learn? 🧠

Using pre-existing components (e.g., through a design system) speeds up the design process!
I leveraged BMO Commercial Banking’s design system Lexicon and decreased lag that would have been spent on designing new UI components and instead got to focus more on the user experience and flow for our fraud analysts, saving valuable time.

Building Smarter Fraud Detection: A 30-Hour Design Sprint at BMO

PROBLEM ⇢ SOLUTION ⇢ IMPACT

At BMO’s annual innovation sprint, we designed an AI-assisted fraud dashboard that helped analysts detect fraud 67% faster — winning Best Use of AWS.

OVERVIEW

🎥 SETTING THE SCENE

Imagine Shark Tank, but BMO-branded.

At Destination Digital 2024, our cross-functional team was challenged in a 30-hour innovation sprint to design an AI-driven solution to help fraud analysts identify counterfeit cheques faster and more accurately.

THE CHALLENGE

Several tools and systems are used by fraud ops team members to determine if a check is fraudulent or legitimate, as opposed to one centralized space.

Fraud analysts were spending up to 15 minutes per cheque, manually verifying signatures, transaction histories, and account patterns.

The process was repetitive, error-prone, and stressful under time pressure, especially with thousands of daily cases.

GOAL

Allow fraud ops analysts to more quickly determine whether or not a deposited check is fraudulent or not.

Use AI to flag suspicious transactions automatically with a web page that flags identified fraudulent/valid deposits with a confidence level from 0 to 100%.

MY ROLE

🔬 Led 3 user interviews with fraud analysts to uncover workflow pain points

🖥️ Designed an interactive Figma prototype of an AI‑driven dashboard

🎨 Developed visual language leveraging BMO’s design system

📢 Created a presentation and live demo for executive judges

How did we learn more about the problem?

We had an ace up our sleeve - a team member who spent years working adjacent with the Fraud Team and knew some fraud analysts personally.

This connection allowed us access to interview 3 fraud analysts via Teams, where we could learn more about their current systems, and the problems they face.

Identifying the core issues

From speaking to the analysts, we were able to find out the following:

There are primarily two types of cheque fraud:

1. account-based/behavioral fraud (e.g. a large deposit on a newly-opened account)

2. physical cheque fraud (e.g. cheque forgery).

Manual processes involve fraud analysts reviewing one cheque, one person at a time, which takes up a lot of time (an average of 15 minutes per case).

The current technology that we have leads to a lot of false positives and missed fraud. The current software is very old and archaic.

Fraud analysts have to manually create rules based on reviewing the analytics and statistics of past and current cases which takes time.

💡 THE SOLUTION

With only 30 hours, we prioritized screens that automated manual review, focusing on analysts’ most common pain point: transaction verification.

From there, I built an AI-assisted fraud dashboard that automatically surfaced high-risk cheques using machine-learning confidence levels.

Now introducing… BMO Fraud Fighter Central!

KEY FEATURES

Home Page - a streamlined view

Fraud analysts could see the number of fraudulent transactions in a given period, graphs showing the statistics of fraudulent transactions by location and by channel, the rule efficiency score, as well as to-do items.

Cheque Review

A fraud analyst would be able to review a singular cheque, with fraud indicators calculated from 0-100 that they could review. Also, they would be able to see that person’s account and any past reviews, notes, alerts, and changes.

Rule Creation

Fraud analysts can create, edit, and delete rules that would create triggers/alerts to potential fraud. Here, we use AI machine learning to suggest rules that analysts may have missed through observation alone.

My Alerts

Fraud analysts can review fraud cases in a table view, view and assign statuses to a case, as well as escalate to a senior analyst if needed.

But how about the customer?

While this is great from the fraud analyst’s view, what if there is a way to further prevent fraud right at the source, from the customer’s vantage point?

I explored the current BMO process of depositing a cheque and introduced two more solutions to detect fraud:

1. Leveraging two-factor authentication

2. Input field verification

Impact

Quote

Results: We won! 🏅

Our prototype won Best Use of AWS at BMO’s Destination Digital 2024. Judges praised the balance of automation and clarity, noting how seamlessly it connected to real analyst workflows.

By using dummy data fed to the data model, we found we could reduce the time spent on each fraud case from 15 minutes to 5 minutes!

This project proved that design and technology together can amplify human judgment in high-risk environments.

Conclusion

What would we redo? ⏪

We did not have a project manager on our team. If we were to redo this, I’d focus more on an execution plan for the project, as well as estimating the funding and resources needed for the project to proceed.

How would we move forward if given the opportunity? ⏩

Given more time, I would conduct usability testing with real fraud analysts to validate flows and expand the data visualization for real-time risk trends on the dashboard.

What did I learn? 🧠

Using pre-existing components (e.g., through a design system) speeds up the design process!

I leveraged BMO Commercial Banking’s design system Lexicon and decreased lag that would have been spent on designing new UI components and instead got to focus more on the user experience and flow for our fraud analysts, saving valuable time.