Clarity — A Clearer Path to Your Next Money Decision

Financial education that goes beyond your bank balance.

Inspiration

Your bank can tell you how much money you have. But can it tell you what to do next?

Every day, people make financial decisions that can shape their futures. Whether it's accepting a new job, moving to another city, paying off student loans, or deciding how much to save, these choices involve trade-offs that aren't always obvious.

For students and young professionals, navigating these decisions can be especially overwhelming. Traditional banking applications are great at showing balances, transactions, and spending habits, but they rarely explain how that information can guide a person's next financial decision.

For example, imagine receiving two job offers: one for $95,000 in New York City and another for $78,000 in Pittsburgh. The higher salary might seem like the obvious choice, but after accounting for taxes, housing, transportation, and living expenses, the financial reality could be very different.

We wanted to create something that helps people understand those differences before making important decisions.

That's why we created Clarity, a financial education and decision-support platform designed to make financial knowledge more accessible, personalized, and actionable.

Our mission is simple: help people move from knowing how much money they have to understanding what they can do with it.

What it does

Clarity is designed to transform everyday financial information into personalized education, meaningful insights, and practical financial planning.

Instead of simply displaying transactions, our platform aims to help users understand their financial circumstances, recognize important changes, and evaluate the potential consequences of different decisions.

Clarity is built around three major experiences:

1. Personalized Financial Education

Financial education shouldn't be one-size-fits-all.

Clarity's personalized learning experience is designed to understand each user's existing financial knowledge and guide them toward concepts most relevant to their situation.

The intended learning process follows four steps:

Assess: Evaluate the user's understanding of topics such as budgeting, saving, investing, retirement, and taxes.
Learn: Identify knowledge gaps and deliver relevant educational content.
Apply: Connect new knowledge to real financial scenarios and decisions.
Improve: Track progress and adjust educational recommendations as the user develops financial confidence.

We've developed backend foundations for assessments, educational content, learning plans, and progress tracking. Our longer-term goal is to turn these components into a connected, interactive learning experience.

2. "Future You" Financial Simulator

What if you could explore the financial consequences of a decision before making it?

Our "Future You" simulator is designed to let users compare possible financial futures through interactive, side-by-side scenarios.

For example, a user evaluating two job offers could compare estimated take-home pay, living expenses, relocation costs, savings growth, and debt repayment timelines.

Rather than providing a single answer, the planned simulator allows users to adjust assumptions such as rent, salary growth, and monthly expenses to understand how different choices affect their financial outlook.

Our goal is to make complicated financial trade-offs easier to visualize while ensuring the underlying calculations and assumptions remain transparent.

The simulator is currently in development, with the broader vision of helping users turn their preferred scenario into a practical financial plan.

3. Proactive Financial Advisor

What if your banking application could recognize when your financial circumstances might be changing and help you prepare?

Clarity's Proactive Advisor is designed to identify potentially important financial moments by analyzing transaction histories and account balance patterns.

Our implemented detection engine can recognize five types of signals:

New employment: A larger paycheck from a new employer may indicate a new job.
Moving: A significant payment to a new housing merchant may indicate relocation or a new lease.
Increased spending: Spending substantially above a recent baseline may signal a need to revisit a budget.
Student loan repayment: The first observed student loan payment may indicate the start of repayment.
Growing savings: Several consecutive increases in account balances may indicate an opportunity to evaluate savings options.

For example, when Clarity detects a possible change in employment, it can generate a message such as:

"Looks like you may have started a new job. Want to plan your first paycheck?"

Rather than simply displaying an alert, the advisor's planning engine can calculate monthly surplus, estimate an emergency savings gap, and suggest a possible savings action based on editable inputs.

Most importantly, Clarity treats financial patterns as clues, not confirmed facts. It provides supporting evidence and is designed to let users verify what actually happened before acting on a recommendation.

The detection and planning engines have been implemented and tested. Integration with the complete application interface remains part of our ongoing development.

How we built it

We developed Clarity using a modular web application architecture, separating the user experience, financial calculations, educational content, and backend data services.

Our approach centers on one principle: financial calculations should be performed transparently in code, with the results explained in language people can understand.

Frontend and User Experience

Next.js and React provide the foundation for the web application.
TypeScript supports structured, maintainable application logic.
Tailwind CSS provides styling tools for building a responsive interface.
Our interface design focuses on simplicity, approachable financial explanations, and clear comparisons between alternatives.

Financial Analysis and Proactive Guidance

Developed a rule-based detection engine in TypeScript to analyze synthetic transaction histories and account balance snapshots.
Implemented five financial event-detection rules covering employment changes, housing payments, spending changes, loan repayment, and growing savings.
Created deterministic calculations for monthly surplus, emergency savings needs, and potential savings actions.
Structured advisor responses to include supporting evidence rather than unexplained recommendations.

Backend and Data Management

Used Python and FastAPI dependencies to establish backend application foundations.
Created SQLite-based repositories for managing user-associated application data.
Developed structures for financial knowledge assessments, learning plans, educational lessons, and proposed financial actions.
Organized backend components to support future expansion and integration with the frontend.

Authentication and Security

Incorporated Auth0-based identity and token-verification foundations.
Designed user-associated records with ownership and access-control considerations.
Separated development demo identities from the intended authenticated application workflow.

Testing and Responsible Financial Data

Used synthetic transaction histories and balance snapshots rather than connecting to real bank accounts.
Implemented automated Node.js tests for the Proactive Advisor.
Tested financial signal detection across different histories, transaction orders, spending thresholds, and incomplete data.
Included planning input validation to help prevent unreliable calculations.

Throughout development, we prioritized explainability, modularity, privacy, and responsible financial guidance.

Challenges we ran into

Making Financial Advice Trustworthy

One of our biggest challenges was recognizing that financial data doesn't always reveal the complete situation.

For example, a larger paycheck might indicate a promotion, a new job, a bonus, or a one-time payment. Making a recommendation based on an incorrect assumption could mislead the user.

To address this, we built our detection system around identifiable financial signals and supporting evidence. Rather than claiming to know exactly what happened, Clarity presents a possible explanation and is designed to let users confirm it.

Creating Explainable Financial Calculations

We wanted our financial tools to produce results that users could understand and evaluate.

That meant separating the mathematical calculations from their explanations, clearly defining assumptions, and allowing inputs to be adjusted without obscuring how the results were produced.

This was particularly important for the financial planning engine, where recommendations involving spending, income, and savings must remain understandable.

Integrating Multiple Components

Clarity combines several technical areas: frontend development, financial modeling, backend data management, authentication, and educational content.

Designing these components to work together while allowing team members to develop features independently required careful planning.

We approached this by building modular foundations and reusable logic, allowing features such as the Proactive Advisor to be tested separately before complete application integration.

Making Financial Education Approachable

Financial planning can become complicated quickly, particularly for users unfamiliar with concepts such as compound interest, emergency funds, and debt repayment.

We had to think carefully about how to present financial information without overwhelming users.

Our guiding question became: How can we make the next financial decision easier to understand without hiding the details that matter?

Accomplishments that we're proud of

We're especially proud of establishing a foundation that brings together financial education, scenario planning, and proactive guidance around a single goal: improving financial confidence.

Some of our most meaningful accomplishments include:

Developing a functioning proactive detection engine: Implemented five financial signals that can identify potentially important changes in synthetic financial activity.
Building transparent planning calculations: Developed logic to estimate monthly surplus, emergency savings needs, and possible savings actions using adjustable inputs.
Creating a personalized education foundation: Structured backend records for assessments, learning content, and individualized learning plans.
Establishing a modular architecture: Organized financial analysis, frontend development, authentication, and data persistence as separate components designed for integration.
Validating behavior through automated testing: Created tests for detection consistency, financial thresholds, incomplete histories, and planning inputs.
Protecting financial privacy in our prototype: Used synthetic data rather than collecting actual banking credentials or accessing live accounts.

More broadly, we're proud of developing a concept that approaches financial technology from a different perspective.

Instead of asking how much more financial data we can show users, we asked how we can make the data they already have more meaningful.

What we learned

Building Clarity helped us understand that creating useful financial technology involves much more than developing mathematical models or designing an attractive interface.

One of our biggest lessons was that accurate financial information is only valuable when people can understand and apply it.

We also learned that:

Explainability builds trust. Users should understand why a financial insight was generated and which assumptions influence its conclusions.
Personalization requires context. Financial behavior can suggest important life changes, but users must remain involved in interpreting those signals.
Financial education is most effective when practical. Learning about budgeting or saving becomes more meaningful when connected to a real decision.
Modular architecture makes development more manageable. Building independent financial engines and backend components helps with testing, debugging, and future integration.
Responsible financial tools must acknowledge uncertainty. Projections and recommendations are estimates, not guarantees, and should never be presented as certain outcomes.

These lessons reinforced our belief that the next generation of financial applications should not only become more intelligent but also more understandable.

What's next for Clarity

Clarity is currently an educational hackathon prototype, but our vision extends beyond the initial implementation.

Our next steps include:

Complete the integrated application: Connect the frontend interface, proactive detection engine, educational content, user accounts, and backend services into one seamless experience.
Expand the "Future You" Simulator: Introduce interactive comparisons for job offers, relocation decisions, debt repayment, major purchases, and long-term savings.
Personalize financial education: Use assessment results and educational progress to recommend lessons relevant to each user's financial situation.
Enhance proactive guidance: Build richer contextual insights, user-confirmed financial events, and optional notifications.
Conduct real user testing: Evaluate whether Clarity improves financial literacy, confidence, and users' ability to understand financial trade-offs.
Explore financial institution integration: Investigate secure, consent-based approaches to connecting Clarity with real financial information while maintaining strong privacy protections.

Our long-term vision is to make Clarity a financial companion that grows alongside its users, helping them navigate increasingly complex decisions as their lives change.

Our Vision

The future of banking should do more than tell you where your money went. It should help you understand where your money could take you next.

We believe everyone deserves access to financial guidance that is understandable, personalized, and actionable.

With Clarity, we're working toward a future where financial applications do more than display numbers. They help people learn from those numbers, explore their options, and build confidence in the decisions they make.

Clarity — A clearer path to your next money decision.

🚀 Explore Clarity!

Clarity was developed for HackPSU Fall 2026, with a focus on improving financial confidence through accessible education, explainable calculations, and personalized guidance.

Explore our source code, development progress, and technical documentation:

GitHub Repository: https://github.com/Krish-Sawant/clarity