FINSENSE

Intelligent Insights for Better Loan Choices

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Inspiration

The idea for FINSENSE actually came from observing how people often don't understand their own financial behavior or loan decisions, especially in periods of financial stress or uncertainty. So many of the financial tools that exist today deal only with the numbers and eligibility but don't explain why one particular decision is good or risky. This drove us to build a system that does not just analyze financial data but provides meaningful and explainable insights that users can really understand and trust.

What it does

Finsense is an AI-driven financial decision support system based on the analysis of a user's financial information, including income and credit score, coupled with information about outstanding loans. It compares new and existing loan options, calculates potential profit or loss based on interest rates, and conveys explainable recommendations that will assist users in making informed financial decisions.

How we built it

We developed the FINSENSE prototype using the set of Python-based technologies by integrating rule-based reasoning and AI-driven analysis. FINSENSE takes the financial inputs from the users in a structured format, applies the eligibility and risk analysis logic to the inputs, and provides the users with suggestions and reasoning. This solution is primarily modeled for simplicity and viability and is implementable without using the banking API.

Challenges we ran into

One of the major challenges was designing logic that accurately compares existing and new loans while keeping the explanations simple and understandable for users. Handling multiple financial parameters without making the system overly complex was another difficulty. Ensuring clarity, consistency, and correctness in financial calculations also required careful testing and validation.

Accomplishments that we're proud of

We are proud of building a system that focuses on explainability rather than just prediction. Successfully integrating existing loan analysis with profit/loss calculations and presenting the results in a user-friendly manner was a key achievement. The project demonstrates a realistic and practical application of AI in the financial domain.

What we learned

Through this project, we gained a deeper understanding of financial decision-making, loan structures, and interest-based analysis. We also learned how AI and rule-based systems can be combined effectively to solve real-world problems. Additionally, the project helped us improve our skills in system design, logical thinking, and building user-centric applications.

What's next for FINSENSE

In the future, FINSENSE can be enhanced by integrating real-time bank data, supporting more loan types, and providing personalized financial improvement suggestions. A mobile application version and advanced analytics such as credit score improvement guidance can further increase its real-world impact and usability.

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