Isolation Forest (IF) Tool
The Isolation Forest (IF) Tool provides a powerful and user-friendly solution for detecting anomalies in the transaction data of a specific agency. By applying the Isolation Forest algorithm, the tool calculates an anomaly score for each transaction, indicating how significantly a transaction deviates from normal patterns observed in the data. Transactions with lower Isolation Forest scores are flagged as potential outliers, suggesting unusual activity such as exceptionally high amounts or infrequent vendors.
The tool analyzes transaction data from the past three years, allowing users to focus on recent trends and patterns. It offers flexibility to filter data based on specific criteria, such as time periods, vendors, or transaction amounts, enabling a more targeted analysis. Once the Isolation Forest scores are calculated, users can define a threshold to identify the most significant outliers. These results can then be exported in JSON or CSV formats, making it easy to integrate the findings into other tools or systems for further investigation.
The Isolation Forest algorithm is particularly effective at detecting both global and local anomalies, even in large and complex datasets. Since it works by isolating outliers, it can identify both extreme and subtle deviations that might otherwise go unnoticed using traditional methods. By combining advanced data analytics with customizable data extraction, the IF tool empowers users to efficiently uncover and analyze suspicious transactions, enhancing their ability to monitor and address potential irregularities.
Typical Thresholds for Anomalies:
Strong Outliers: ≤ -0.2 to -0.5
Moderate Outliers: between -0.2 and 0
Normal Data Points: > 0