FinSight：以開源 AI 實現更智能的財務分析

FinSight: Open-Source AI for Smarter Financial Analysis

Submission for Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

What I Built

FinSight is a financial anomaly detection and reporting tool designed for freelancers, accountants, and small businesses.

It combines three detection methods:

Rule-based detection: Flags duplicates and invalid entries.
Statistical analysis: Uses Z-scores to identify unusual values.
Machine learning: Uses Isolation Forest to detect unusual transaction patterns.

FinSight also provides financial dashboards, profit and loss analysis, balance sheet validation, variance analysis, and PDF reports.

The goal is to make financial analysis more accessible without expensive proprietary AI services.

Demo

FinSight uses a synthetic dataset of 1,000 transactions to demonstrate its workflow.

The dashboard visualizes financial metrics and detected anomalies, while PDF reports make the results easier to review.

Code

GitHub: FinSight

Built with Python, Pandas, scikit-learn, Streamlit, Plotly, SQLite, SQLAlchemy, and ReportLab.

How I Built It

I built FinSight using a modular Python architecture.

The pipeline combines deterministic financial checks, Z-score analysis, and Isolation Forest, an unsupervised machine learning algorithm from scikit-learn.

Streamlit powers the interactive dashboard, Plotly handles visualizations, SQLite supports local storage, and ReportLab generates PDF reports.

Why Does Open Innovation Matter?

Open-source machine learning makes FinSight more affordable and customizable.

Lower costs: Local inference avoids per-request AI API charges.
Privacy: Financial data can be processed locally instead of being sent to an external AI service.
Transparency: The detection pipeline can be inspected, tested, and improved.
Flexibility: The model and detection rules can be adapted to different business needs.

For a small business, an affordable tool that keeps financial data under its control can be more practical than an expensive cloud-based solution.

What's Next?

My next steps are to improve anomaly explanations, support more transaction formats, customize detection rules, and test FinSight with freelancers and small business owners.

The goal is to turn an open-source machine learning project into a practical tool that solves a real financial problem.