Station Bank
Clara the Investment Chatbot

Overview

Many Millennials are at the point in their lives where they have some disposable income and clear financial goals. They are interested in investing but don’t really know where to start. They’re worried it’s too risky, and a general distrust for traditional banks leads them to seek advice from knowledgeable friends or to research things themselves. Clara the investment chatbot helps Millennials build confidence in investing as a tool to reach their financial goals.

Project Duration: 1 Week
Platform: iOS
Team Members: Jessica Walker
Keri Jordan
Jackie Lee
Margaret Kalejta   

Problem Space

We were assigned the following challenge by the fictional Station Bank: customers have been asking for a native mobile app for a long time, they can already manage their personal finances through our responsive website and we want a mobile app offering to provide value that uniquely speaks to our target customers.

To address the problem, we focused in on Station Bank’s young professional (Millennial) demographic. We selected investing as the focus for the mobile app offering. In order to gain a more empathic understanding of the problem and our demographic we researched Millennials investment needs and the barriers they face. We found that Millennials are putting their money into savings, rather than investing, and do not feel connected to traditional banks:

Interviews

To validate our findings and to extract further insights as to why Millenials are not investing we interviewed six individuals between the ages of 22 - 35. Each team member wrote their findings onto sticky notes and together we categorised them into themes on a whiteboard.

Quotes

Insights on Millennials and Investing

We gained the following key insights from our interviews:

• Majority of banking done online.    

• Have some disposable income and clear financial goals, such as saving for a downpayment on a condo.  

• Are interested in investing but don’t know where to start & think it’s too risky.

• Would rather seek financial advice from a knowledgeable friend than a bank.

Persona

Based on the needs and wants of our interviewees, we developed our persona Henry.

Henry sacrifices his independence to live at home in Mississauga with his parents because he believes in saving towards his future. His ultimate goal is to save enough to purchase a condo in 5 yrs. He’s a loyal customer of Station Bank but feels like the money in his savings account isn’t growing fast enough. He’s heard his friends talk about investing and he’s wondering if there’s a bank out there with an easy solution.

He also has some distrust for advisors because he worried that they don’t have his best interest at heart. He’s heard stories about hidden fees, and is worried that they’ll be judgemental about his financial situation.

Ideation

Based on the teams cumulative insights we wanted to focus in on one main idea to help millennials feel more confident and gain knowledge in investing.

Each team member sketched a proposed solution to our How Might We which we posted anonymously on the board. Using stickers, we marked the most promising solutions and parts of other solutions that we found valuable.

The chosen solution was an investment chatbot which we named Clara. Clara is a conversational AI that uses machine learning to guide new investors through their financial goals and match them to an investment portfolio. Clara acts like a knowledgeable friend and researcher with access to large amount of data.

Storyboarding

Next, we sketched out the journey of how Henry would come to meet Clara. He’s an existing Station Bank client so he first encounters Clara when he’s looking at his savings account.

We further developed a persona for our Chatbot Clara. With a warm, empathetic, responsive, and responsible demeanour, Clara’s an expert in breaking down complex ideas and her main goal is to help clients like Henry become more confident and knowledgeable in investing.

Clara’s Persona

Copy

We used Clara’s persona to develop the copy for Clara.

UI Components

The main UI Components were provided by Station Bank:

Testing

When we finished the Copy, Wireframes and UI components in Sketch, a Prototype was created in InVision and tested on Users (V1). In total we tested five users, collecting their insights, validations, and feedback. We then used this information to iterate and improve the apps usability (V2).

Hi-Fi Prototype

Validated Concepts

During our testing we validated that Millennials are very interested in investing but need guidance. Users were comfortable with Clara and their confidence increased based on Clara’s conversational tone, ability to predict their hesitations with investing and ability to base her knowledge on data.

We further found that Millennials trust computer processed data as a way to get more accurate data. It compiles information in a way that a person may not be able to. A few users expressed that they wanted the option to pull the information on their own without Clara’s assistance. They wanted to know more about the source of the data.

Next Steps

For the purpose of the assignment we made the assumption that the user had limited investing knowledge. Ultimately Clara would adapt to help users with various levels of investment knowledge. The user should be able to pick where they start instead of being automatically treated like a novice.

Clara should offer more transparency into how she is collecting data and preforming real time calculations in order to build trust and validity with the user.

The balance of Clara’s responses having too much text to too little information needs to be further explored. Users’ interest in investing with Clara’s will decrease if they feel that their questions would be answered more directly by a person.