Summary of Unit8 Talks #7 - Fraud detection - A guide to building a financial transaction anomaly detector

Summary of Unit8 Talks #7 - Fraud Detection: A Guide to Building a Financial Transaction Anomaly Detector

The presentation focuses on building a Financial Transaction anomaly detection system to identify fraudulent activities using machine learning techniques. The primary concepts discussed are fraudulent transactions and anomaly detection, which aim to differentiate legitimate transactions from fraudulent ones in large datasets. Anomaly detection is defined as the process of identifying data points that deviate significantly from the majority of the data.

Key Steps in Building the Anomaly Detector:

• Understanding the Problem:
  • Define the nature of fraudulent transactions, such as those involving stolen credit cards or identity theft.
  • Recognize that fraudulent transactions are a minority in a dataset dominated by legitimate transactions.
• Data Understanding:
  • Analyze the dataset to identify potential inputs for the model and understand the data distribution.
  • Formulate hypotheses about what constitutes an outlier.
• Feature Creation:
  • Select raw data fields and derive new features through feature engineering to enhance the model's predictive capabilities.
  • Features include transaction amount, type, and changes in account balances.
• Model Selection:
  • Choose an appropriate machine learning model for anomaly detection. The presentation focuses on the Isolation Forest model due to its effectiveness in unsupervised learning and ability to handle large datasets.
• Model Training and Evaluation:
  • Train the model on the entire dataset without labels to avoid overfitting.
  • Evaluate the model using metrics like the Area Under the ROC Curve (AUC) to assess its performance against naive baselines.
• Explainability:
  • Implement an explanation model (using SHAP values) to interpret the outputs of the anomaly detection model, providing insights into why certain transactions were flagged as anomalies.
• Real-World Application:
  • Discuss how similar AI systems are used in various industries to detect fraudulent activities in real-time and flag suspicious transactions for further investigation.

Conclusions:

The presentation emphasizes the importance of automated anomaly detection systems in managing large volumes of transaction data and the need for explainability to ensure trust in the model's decisions. The results showed that the Isolation Forest model outperformed naive methods, highlighting its effectiveness in detecting anomalies.

Speakers:

• Franchesco (Main presenter)
• Maxim (Co-presenter)
• Amir (Moderator)

Notable Quotes

— 05:10 — « We really are trying to find the proverbial needle in the haystack. »

Category

Educational

Video