My Works
AI Chatbot in Finance (End to End Design Process)
About
This project explores the design of an application that helps young adults build sound money management habits, while investigating the potential of AI chatbots to provide effective financial guidance. Although developing healthy financial habits early is beneficial, challenges such as student debt and financial complexity often make saving and investing difficult for young adults. Building on insights from existing research, this project aims to ideate a solution tailored to users aged 18–30, supporting them in understanding and improving their financial behaviors.
Aim & Objectives
Gain insights into the behaviour and needs of young adults when it comes to financial management
Explore the potential of utilizing ChatGPT to provide effective financial advice
Ideate a solution that caters to the specific requirements of young adults (18-30), helping them understand and improve their financial habits
Design Method
Technology Research
The rise of AI chatbots, powered by Natural Language Processing (NLP), is transforming the financial industry. These chatbots can engage in conversations that feel like interacting with humans, offering real-time responses, gathering data and providing personalized assistance. Their usefulness extends to various sectors beyond finance. In the financial realm specifically, they serve as valuable resources, helping users understand different financial products and services.
To make them effective, it’s crucial to understand user intentions and preferences while ensuring compatibility with their daily routines. Furthermore, these chatbots have the potential to offer financial guidance through robo-advisors. These robo-advisors are capable of analyzing individual account balances, setting goals for users and optimizing budget allocations accordingly. By combining AI chatbots with robo advisors in this way, individuals can gain the knowledge needed to make well-informed financial decisions based on insights derived from a comprehensive analysis of user behaviour.
User Interview
Conducting user interviews is a valuable way for researchers to gather information about the project’s requirements and gain insights from the intended users. These face-to-face interactions provide an opportunity for researchers to delve into topics in-depth, gaining a better understanding of users’ needs within the specific context.
Interview questions focused aspects:
Users’ cognition toward financial management
Users’ experience of using financial tools/ seeking financial advisors
Users’ intention of engaging with financial AI chatbot (after evaluation)
After coding the transcripts of user interviews, we can use an empathy map to organize and analyze the valuable insights shared by the participants. The empathy map acts as a helpful tool that allows us to move from raw interview responses towards gaining a deeper understanding of the emotions, perspectives and behaviours of the individuals involved.
AI Chatbot Evaluation
To enhance direct user interaction with a financial chatbot, we conducted research on creating a personalized AI chatbot. We effectively developed a demonstrative AI chatbot using the open-source code from AI Advantage. The chatbot comes equipped with user-friendly input and output features and can be accessed on both desktop and mobile platforms. By incorporating the OpenAI GPT 3.5 model, the chatbot stands out as a distinct financial advisor.
During the AI chatbot evaluation, participants were encouraged to interact with our financial AI chatbot by asking any finance-related queries. This allowed us not only to observe their interactive behaviour but also to provide valuable insights for future fine-tuning.
Examples of users’ engagement with AI chatbot
Personas
From the preceding phase, it becomes evident that the empathy map embodies two distinct personas. In order to enhance the ability to empathize with our users, we have developed two personas, each accompanied by a concise biography, goals, motivations, and points of frustration.
A clear difference emerges between the two personas: users with stable income tend to focus on investing and future planning, while those with limited financial resources—primarily students—prioritize making the most of their current funds. Despite this difference, both personas share a need for support in tracking spending and feel overwhelmed by the amount of financial information available.
When considering solutions that address both needs, it becomes clear that Emily may eventually face challenges similar to Andrew’s as her income grows. This highlights how user needs evolve, reinforcing the importance of designing an application that adapts to users at different financial stages.
Point of Views (POVs)
I | Needs | Insights |
|---|---|---|
Emily is a student with a limited budget. | needs to keep track of her balance effectively and ensure she sets aside enough for living expenses | so she can make the most out of her money by allocating it wisely and avoiding overspending before the month ends. |
Alice is an individual who noted down the spending after making her purchases using paper money | needs a better system to track her daily expenses using physical payment. | so she can gain better control over her finances and save time on manual calculations. |
Coral is a student who views budgeting as a somewhat burdensome task | needs a more joyful and engaging approach. | so she can embrace budgeting and make it a positive part of her daily routine |
William is a marketing analyst with a busy schedule | needs to stay updated on relevant information to target stocks and funds more effectively | so he can earn money through his investments. |
Catherine has a joint account with her partner and sometimes transfers money between accounts | needs a solution to keep track of her expenses, categorize them accurately, and provide flexibility to correct any wrong records | so she can have a clear understanding of her spending across different categories. |
Rapid Idea Generation
Based on the HMW questions, numerous ideas were rapidly generated within a constrained timeframe. Subsequently, the idea that seamlessly aligned with the HMW questions was identified. These selected ideas were marked as possible solutions by attaching a sticky note next to them.
HMW questions:
How might we help her set aside money for living before she overspends it?
How might we create a delightful and enjoyable experience of saving money?
How might we motivate the user to consistently follow and adhere to her budget?
How might we help her track her physical payment?
How might we save his time on researching the latest economic/stock news?
How might we offer users flexibility when everything is automatically synced?
How might we assist the users on money management?
How might we notify users about their remaining balance effectively?
How might we provide users with engaging information about financial literacy?
How might we make the interface simple but essential?
Design
Lo-fi Prototype
To enhance the idea’s clarity and substantiate its viability with user feedback, a low-fidelity prototype was created, encompassing key features: Analytics, Robo-advisor, and the Reward system.
Evaluate
Exploratory test
An exploratory test is employed to assess the effectiveness of a design in addressing users’ needs. In this study, five users were invited to participate in two separate exploratory tests with a lo-fi prototype.
“Three to five users were selected for most of our evaluation due to the cost-efficient way to get the most optimal results that are similar to testing with 15 users.”
– Nielsen’s studies on <Why You Only Need to Test with 5 Users>
Key insights:
Users expressed the desire for added security through two-factor authentication when opening the app to protect their accounts.
Users indicated the convenience of having an overview of all their accounts on the homepage for quicker access and efficient management.
Users expressed interest in the option to track their account balance over specific periods, such as vacations, to better handle their finances.
Users would find it valuable if the Robo-advisor feature could tailor information based on their experience level, ensuring more relevant insights.
Users mentioned that a premium subscription option in the Reward system would allow them to customize goals and discourage misuse for rewards.
Design
Mid-fi Wireflow
After confirming the feature concepts, the next phase involves crafting an inclusive wireframe encompassing all user flows. The mid-fidelity wireframe primarily serves the purpose of functionality testing.
Style Guide & Design System
Hi-fi Prototype
In this high-fidelity prototype, we’ve enhanced the onboarding experience to empower users to explore the app’s functionalities before setting up their accounts, giving them an initial overview of what features are available. Moreover, we’ve improved the clarity of the featured functions during the onboarding process by mapping out key features and replacing illustrations with screen highlights. Recognizing the importance of social presence in user interactions, we’ve refined the personalization process of the AI assistant to emulate real interactions between customers and financial assistants.
Interactive Prototype:
Future work
Regarding future development, valuable insights derived from user feedback during the evaluation of the hi-fi prototype have shed light on avenues worth further exploration. These possibilities include investigating the feasibility of integrating investment accounts, enhancing the presentation of analytics and incorporating insights gathered from the user community. Each of these areas holds great potential for progress and expansion.
