Ananya’s Finance Buddy: a high school student’s AI-Powered Personal Finance Project

Introduction: A High School Student’s AI-Powered Personal Finance Project

Most students learn finance as formulas, definitions, or abstract case studies. Budgeting examples are clean, predictable, and disconnected from real life. But personal finance is rarely neat. Income fluctuates. Expenses are emotional. Decisions are often made with incomplete information.

Admissions officers understand this. They also understand that finance, like healthcare, is a high-stakes domain. Mistakes matter. This is why AI projects grounded in personal finance stand out when done correctly. They show not only technical ability, but judgment.

Finance Buddy, an AI-powered personal finance assistant built by Ananya Gangwar, addresses a problem that nearly every adult faces but few students attempt to solve seriously: helping people understand and manage their money using data-driven insight rather than guesswork.

Why personal finance is a legitimate AI problem

Finance is often misunderstood as spreadsheet work or investment theory. In reality, personal finance is a behavioral data problem.

According to a 2023 Federal Reserve report, nearly 40 percent of U.S. adults would struggle to cover a $400 emergency expense. Lack of financial literacy is not about intelligence. It is about access to guidance and tools that translate numbers into decisions.

AI becomes relevant when:

• Spending patterns are hidden in raw data

• Humans miss long-term trends

• Advice needs to adapt to individual behavior rather than generic rules
  

From an admissions perspective, a student who recognizes this and attempts a responsible solution demonstrates applied thinking that goes beyond coursework.

What makes an AI finance project meaningful (and what doesn’t)

Not every finance-related project is strong. Many fall into predictable traps.

Weak finance projects usually:

• Hard-code rules instead of learning from data

• Ignore user context

• Provide generic advice

• Treat finance as math, not behavior
  

Strong finance projects show:

• Data ingestion and cleaning

• Pattern recognition over time

• Personalized outputs

• Clear boundaries around what the system can and cannot advise

Finance Buddy belongs in the second category.

Case study: Ananya Gangwar’s Finance Buddy

Ananya Gangwar approached AI with curiosity but without assuming it was an all-knowing solution. Like many students, she initially viewed AI as something impressive but distant. The challenge was understanding how AI systems actually help people in practical ways.

The problem she chose

Many individuals track income and expenses but still struggle to understand:

• Where their money actually goes

• Which habits are holding them back

• How to plan realistically for short- and long-term goals

The goal of Finance Buddy was to bridge this gap by turning raw financial data into understandable guidance.

What Finance Buddy does

Finance Buddy is an AI-powered personal finance assistant that allows users to:

• Upload CSV files containing income and expense data

• Manually input financial information

• Analyze spending behavior over time

• Receive personalized insights based on habits and goals
  

Rather than presenting static charts, the system focuses on interpretation. It answers questions like:

• Are certain expense categories growing unintentionally?

• Is the user’s saving pattern aligned with stated goals?

• Where can small changes have outsized impact?
  

Technical approach and tools

The project uses a practical AI workflow:

• Structured data processing using Python and Pandas

• Pattern analysis to identify trends and anomalies

• AI-driven logic to generate personalized financial insights

• Streamlit to deploy the application in an interactive, user-friendly format
  

Why this project resonates with admissions officers

Selective colleges are not impressed by surface-level automation. They look for evidence that a student understands why a problem exists and how technology can responsibly help.

Finance Buddy demonstrates several qualities admissions officers value:

• Applied data thinking

  The project works with real, messy financial inputs rather than curated examples.

• Human-centered design

  The focus is on helping users understand their behavior, not just showing numbers.

• Scope discipline

  The system provides guidance without pretending to replace financial advisors.

• Clear narrative

  The student can explain the problem, the solution, and its limitations.
  

These qualities are difficult to fake and easy to recognize.

Comparing Finance Buddy to typical high school AI projects

Typical AI Project	Finance Buddy
Tutorial-driven	Problem-driven
Static datasets	User-provided real data
One-size-fits-all output	Personalized insights
Minimal context	Behavioral and financial context
Low admissions signal	Strong admissions signal

This comparison highlights why personal finance AI projects carry weight when executed thoughtfully.

The hidden skill Finance Buddy develops: judgment

AI education is often framed around tools and models. What matters more is judgment.

Finance Buddy required decisions about:

• What data is meaningful

• What advice is appropriate

• How to communicate uncertainty

• When not to automate a decision
  

These are the same decisions students face later in college research, internships, and real-world engineering roles. Admissions officers recognize this maturity.

FAQ

1. Is finance too “soft” for AI projects?

No. Finance combines quantitative data with behavioral insight, making it ideal for applied AI.

2. Do colleges prefer finance projects over pure coding projects?

Colleges prefer well-executed projects, regardless of domain. Finance projects stand out because of real-world relevance.

3. Does a project like this help non-business applicants?

Yes. It supports applications in computer science, data science, economics, and interdisciplinary programs.

4. Is guided mentorship necessary for projects like Finance Buddy?

Guided mentorship helps students avoid shallow implementations and build systems with real reasoning behind them.

Closing perspective: why Finance Buddy matters

From an evaluator’s standpoint, Finance Buddy represents a shift from learning AI as a subject to using AI as a tool for understanding human behavior. That shift is exactly what selective colleges look for.

Ananya Gangwar’s project shows how technical curiosity, when paired with structure and responsibility, leads to meaningful outcomes.

Programs like BetterMind Labs are designed to support this level of work by combining mentorship, real engineering workflows, and outcome-driven projects. For families evaluating serious AI pathways, exploring structured programs at bettermindlabs.org is a rational next step when the goal is depth rather than credentials.

Interested in More? Read Why Your High School Student Needs a Passion Project This Summer