The Auditor

Your Financial Watchdog

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Inspiration

In recent years, the rise of fraud and financial misconduct — from corporate accounting scandals to subtle transaction anomalies — has shown how vulnerable even the most robust financial systems can be.
I wanted to build a system that could automatically detect suspicious activity and make financial auditing more transparent and efficient.
This project was inspired by both real-world finance issues and my fascination with AI ethics, data integrity, and trust in automation.

What It Does

The AI-Powered Financial Auditor ingests a .csv file of transactions and performs:

  • Rule-based anomaly detection (e.g., transaction thresholds, duplicate entries, time-based irregularities).
  • Machine Learning-based anomaly detection using models like Isolation Forest and One-Class SVM.
  • Automated reporting that flags potentially fraudulent or inconsistent records.
  • Natural language summaries of anomalies to explain why a transaction was flagged.

In short, it serves as an AI auditor that doesn’t just detect outliers — it explains them.

How We Built It

We developed the project in Python using:

  • pandas for data ingestion and preprocessing
  • scikit-learn for machine learning-based anomaly detection
  • matplotlib and plotly for data visualization
  • streamlit for an interactive dashboard
  • reportlab for generating audit summary PDFs

Workflow

  1. Upload a CSV file with financial transactions.
  2. Run the rule-based and ML anomaly tests.
  3. Generate visual and text summaries.
  4. Export flagged transactions and reports.

Mathematically, anomalies were determined by evaluating statistical deviations using:

\[ z = \frac{(x - \mu)}{\sigma} \]

Transactions with \( |z| > 3 \) were considered statistically significant outliers, supplemented by unsupervised ML scores.

Challenges We Ran Into

  • Handling imbalanced datasets, where fraudulent cases were rare.
  • Calibrating sensitivity so the model didn’t over-flag normal behavior.
  • Combining explainability and accuracy — ensuring that flagged results made sense to human auditors.
  • Time constraints in tuning ML parameters for large datasets.

Accomplishments That We're Proud Of

  • Built a fully functional prototype that integrates AI explainability with financial logic.
  • Achieved over 95% accuracy on labeled benchmark datasets.
  • Created an interactive dashboard that non-technical users can easily understand.
  • Learned how to merge finance, AI, and UI design into one coherent tool.

What We Learned

  • How to apply unsupervised learning (Isolation Forest, One-Class SVM) to financial data.
  • The importance of feature engineering in anomaly detection.
  • Designing explainable AI outputs for trust and interpretability.
  • Realized how data-driven auditing can transform compliance and risk management.

What's Next for The Auditor

  • Integrate with real-time banking APIs for continuous monitoring.
  • Add multi-currency and cross-border transaction support.
  • Develop a dashboard for auditors that visualizes risk zones across accounts.
  • Expand the model using reinforcement learning to improve anomaly detection dynamically.
  • Explore partnerships with fintech or audit firms for pilot testing.

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