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Creating an End-to-End Application

Ai Powered Budgeting Apps: Shaping the Future of Financial Management 



This case study chronicles the design and development of a mobile budgeting app that leverages cutting-edge Artificial Intelligence (AI) to revolutionize personal finance management. Recognizing the growing need for financial literacy and personalized guidance, this project envisioned a future where budgeting goes beyond simple tracking to become a comprehensive and intelligent financial companion.

Understanding User Needs:

Extensive research, including user interviews, revealed several key insights regarding personal finance:

  • Lack of Education: Many individuals expressed a desire for financial literacy education, highlighting a gap in traditional schooling.

  • Generational Challenges: Budgeting struggles transcend age groups, impacting both young and seasoned earners.

  • Behavioral Patterns: Financial habits often stem from family dynamics, creating ingrained patterns that can be difficult to break.

  • Complexity Beyond Basics: While the core concept of "spend less than you earn" seems straightforward, the practical application proves challenging for many users.

  • Income Disparity: Budgeting difficulties persist across income levels, affecting both high and low earners.

  • Tedium and Frustration: The tedious nature of budgeting often leads to user abandonment.

These insights became the driving force behind the design, shaping an app that addresses not just the "what" but also the "how" of managing finances effectively

  • UX Designer

  • UX Researcher

  • Interaction Designer

  • UI Designer

  • Figma/Figjam

  • Interviews

  • Market Research

  • Competitive Analysis

  • Affinity mapping

  • Impact Effort Matrix

  • Moderated & Unmoderated Usability Testing


The initial research phase embarked on a mission to delve into the financial psyche of potential users. Through user interviews, we sought to understand their:

  • Financial Attitudes: What are their core beliefs and perspectives towards money management?

  • Budgeting Practices: Do they currently employ any budgeting methods? If so, what are the strengths and weaknesses of their existing approach?

  • Spending Behaviors: How do users typically make purchasing decisions? Are there recurring patterns or areas of concern?

  • Spending Philosophies: What are their underlying principles regarding money allocation and financial goals?

By delving into these aspects, we aimed to create a comprehensive user profile and identify common pain points and challenges associated with budgeting.

  1. User Research: Uncovered demand for financial literacy education and the universality of budgeting struggles across demographics.

  2. Key Pain Points: Users grapple with translating budgeting knowledge into action and find traditional methods tedious.


Leveraging affinity mapping, we delved deeper into the research data, visually clustering user needs and pain points based on similarities. This process revealed two distinct user segments with unique financial personalities:

1. The Financial Savvy User:

  • Financial Confidence: Demonstrates a strong understanding of financial concepts and actively seeks tools to optimize their financial well-being.

  • Goals and Needs: Prioritizes features like investment tracking, detailed financial reports, and advanced goal-setting functionalities.

  • Frustrations: May find basic budgeting apps too simplistic or limiting in functionality.

2. The Budgeting Beginner:

  • Financial Anxiety: Feels apprehensive about financial matters and lacks confidence in budgeting abilities.

  • Challenges: May have limited experience with budgeting apps or struggle with maintaining consistent budgeting habits.

  • Motivations: Seeks guidance and simplified budgeting to overcome anxieties and achieve financial goals.

Understanding these distinct user profiles was crucial. It allowed us to design an app that caters to both segments:

  • Intuitive Interface: A user-friendly interface that is approachable for "Budgeting Beginners" while still offering features for "Financial Savvy Users."

  • Progressive Learning: The app could utilize AI to offer educational resources and tutorials specifically tailored to address the anxieties and knowledge gaps of "Budgeting Beginners."

  • Customization Options: Users could personalize the experience by selecting features and functionalities that best suit their financial goals and confidence levels.

By segmenting users and identifying their specific needs, we ensured the app would be not only user-friendly but also provide valuable tools and resources for both "Financial Savvy Users" and "Budgeting Beginners" to achieve their financial objectives.

  1. Affinity Mapping Revealed User Profiles: Leveraged affinity mapping to identify two distinct user groups: Financially Savvy Users and Budgeting Beginners.

  2. Financial Savvy Users: Seek advanced features like investment tracking and detailed reports, potentially finding basic budgeting apps limiting.

  3. Budgeting Beginners: Feel apprehensive about finances and lack confidence. Desire guidance and simplified tools to overcome anxieties and achieve financial goals.

Affinity Map
Average User.jpg
Educated User.jpg


The ideation phase involved conducting a competitive analysis of existing budgeting apps. This involved:

  • Downloading and Exploring Apps: I downloaded several popular budgeting apps to gain firsthand experience with their features and functionalities.

  • Identifying Trends and User Insights: While exploring these apps, I took note of features I found beneficial or areas for improvement. This helped me gather valuable user insights that would inform the design of my own app.

  • Creating User Flows: Equipped with these insights and my envisioned features, I crafted three user flows:

    • Sign Up Flow: This flow would map out the process for new users to create an account and onboard seamlessly.

    • Existing Account Flow: This flow would detail how established users navigate the app to access their financial data, track spending, and manage their budgets.

    • Demo Flow: This flow could potentially showcase the app's core features and functionalities for users who are hesitant to commit but curious about the app's capabilities.

By analyzing existing solutions, I was able to identify user needs and build upon existing design patterns. This competitive analysis, coupled with user research findings, provided a solid foundation for crafting unique and impactful user flows for the AI future-focused budgeting app.

  1. Competitive Analysis: Downloaded and explored popular budgeting apps to identify trends and user insights.

  2. User Flows Defined: Created 3 user flows: Sign Up, Existing Account, and Demo to address onboarding, account management, and app exploration needs.

User flow- Sign up.jpg
User flow- Existing account.jpg
User flow- Demo flow.jpg

UI Design

I dove into UI design with a user-first approach. Starting with a low-fidelity wireframe for the onboarding flow helped me map out the user journey before getting into visuals.

The goal was to create a balance between professional and welcoming to attract average users who might be intimidated by budgeting apps. User research played a major role here. Learning that users needed a simple and clear interface was key because I knew I need a design that would avoid overwhelming new users.

With the low-fidelity wireframe complete, I moved on to a mid-fidelity design for onboarding, incorporating user research and design goals. To speed up future design, I also developed a component set for reusable elements.

Choosing the right color palette was important for the app's brand. I opted for orange to represent action and blue for a calming and trustworthy feel.

Focusing on User Experience:

To boost my design process, I used ChatGPT to generate user-friendly copywriting. This copy provided a realistic sense of voice and tone for the app, which helped me visualize the final user experience and focus my design efforts. Finally, I chose the Inter font for its clean and professional look to ensure readability.

What I learned:

This phase hammered home the importance of user research and a user-centered approach. Initially, translating my vision into a concrete app felt daunting. However, by leveraging user research and carefully selecting the right flow to begin with, I established a solid foundation for the design and ensured it aligned with user needs.


UI Design: User-First Focus

  1. Lowfi: Started with onboarding flow.

  2. Balancing Act: User research informed a midfi design that helped avoid overwhelming new users.

  3. Design Efficiency: Developed a component set for design efficiency.

  4. Brand Identity: Chose an orange & blue color palette (action & trust) to establish the app's brand.

User Experience Focus:

  1. Enhanced Design: ChatGPT for user-friendly copywriting, improving design visualization for myself.

  2. Readability Priority: Selected Inter font for optimal readability.

Low Fidelity
Sign up flow- lowfi.jpg
Mid Fidelity
Mid fidelity sign up flow.jpg
Component Set
Component set.jpg
Iterations & High Fidelity

Color, Brand Identity, User Friendliness:

While initially drawn to orange, I wasn't convinced it effectively conveyed the welcoming atmosphere I envisioned for the brand identity. Seeking feedback, I consulted both my mentor and friends. Their insights, along with my own evaluation, led me to switch to a blue color palette. This change proved successful in achieving the desired user-friendly feel.


In early stages, I explored logo creation tools to jumpstart the design process. However, collaborating with my mentor highlighted the value of a graphic designer for next steps. We also discussed the impact of color psychology on user perception, ultimately selecting blue to evoke feelings of trust and inviting to align with the brand identity. While a placeholder name "logoipsum" served for this initial design, the importance of a user-friendly app name is well-recognized. Future iterations would prioritize exploring and testing potential names with target users.

Remaining User Flows:

Following the color refinement, I focused on designing the second and third user flows,  "Category Selection" and "AI Mentor" feature. Completing these flows involved meticulous pixel-perfect adjustments to ensure a cohesive final design.

Personalized Onboarding 
  1. Color Iteration: Initial orange palette was replaced with blue to achieve a more welcoming user experience, based on feedback and self-evaluation.

  2. User Flow Completion: Designed the remaining "Category Selection" and "AI Mentor" flows, ensuring pixel-perfect alignment for a cohesive final design.

Pre-signup goal selection wasn't just for looks – I envisioned it creating a personal connection from the start. Users could define their financial goals, fostering a deeper understanding of the app's purpose. My mentor reinforced this idea, highlighting the potential for user buy-in. We also explored adding goal descriptions on the next screen, acting as both a welcoming guide and a source of clarity during onboarding. This combination aimed to create a more engaging and user-friendly experience.
Mid fidelity Sign up flow #2.jpg
Full Flow

Prototype & Usability Testing

I tested the prototype with 6 users to gauge its usability. The group included people from various backgrounds and with different levels of tech experience.


  • Two tests were conducted in-person, while the remaining four were done over Whatsapp video chat.

  • After each test, I interviewed the users about their experience with the app. I took detailed notes, capturing their quotes and summarizing their feedback.

  • To organize the feedback effectively, I created an affinity map. This map helped me categorize user insights by topic and similarity, making it easier to identify recurring issues.


Prioritizing User Needs:

Next, I used an impact effort matrix to prioritize the user feedback. This matrix helped me determine which suggestions had the most significant impact on usability while requiring minimal design effort.


Based on this analysis, I implemented two  findings:

  • I added an "add expense" button directly to the home page for easier access.

  • I enlarged some of the buttons within the app to improve their usability.

  1. Tested Prototype: 6 users (diverse backgrounds/tech skills) evaluated usability via in-person & video chat sessions.

  2. Analyzed Feedback: Interviews & affinity map helped categorize user insights.

  3. Prioritized Improvements: Impact effort matrix.

  4. Implemented Changes: Added "add expense" button & enlarged buttons for better usability.

Affinity map & impact effort matrix.


Click on the grey background  to reveal 'expand button' on top right for a full screen experience, and click anywhere within the prototype screen for the blue highlight guide (hard to see blue on blue)

Opportunities & Next Steps

This budgeting app project provided a valuable platform for exploring user-centered design principles. By prioritizing user needs throughout the process, I successfully created a high-fidelity Figma prototype that facilitates intuitive interaction. Here are some key areas for further development:


Option 1: Expanding the User Journey:

  • While this project prioritized core functionalities, further development can optimize user experience by incorporating additional user flows. Designing flows for setting savings goals and tracking joint finances could be particularly valuable. Additionally, these flows can seamlessly integrate the app's AI features, allowing users to leverage automated expense categorization and personalized financial insights for smarter budgeting decisions.

  • User Onboarding Optimization: Optimizing the user onboarding experience could be crucial for user retention. This could involve exploring interactive tutorials, personalized welcome screens, or gamification elements to engage new users and guide them through the app's features effectively.

Option 2: Exploring Future Iterations:

  • Data-Driven Design Decisions: A/B testing after launch could provide valuable data insights on user behavior and feature usage. This data could guide future design iterations, focusing on optimizing elements with lower engagement or redesigning features that users find confusing.

  • Advanced Analytics Integration: Integrating advanced analytics tools could provide deeper insights into user behavior within the app. This data could reveal usage patterns, identify areas for improvement, and inform future design decisions to enhance overall user experience.

  1. Expanding User Journey: Design additional user flows (savings goals, joint finances) to leverage AI features for smarter budgeting.

  2. User Onboarding Optimization: Explore interactive tutorials, personalized welcome screens, or gamification for improved user retention.

  3. Data-Driven Design Decisions (Future): A/B testing to guide future iterations and optimize user engagement.

  4. Advanced Analytics Integration (Future): Leverage analytics to understand user behavior and inform future design choices.

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