Who are the top 10 users with the highest anomalous expense ratios?

Justification:

This query identifies the top 10 users with the highest proportion of "Expense" transactions flagged as anomalous. By examining each user’s total transaction count, number of flagged anomalies, and anomaly ratio, we gain valuable insights into the subset of users experiencing the most irregular expense activity. These insights are critical for enhancing user support, refining fraud detection algorithms, and potentially collaborating with financial institutions to improve security measures. For example, users with consistently high anomaly ratios may require closer monitoring or targeted security prompts to ensure legitimate spending activity.

Moreover, this data could be valuable to third-party partners, such as fraud detection services or financial institutions, interested in understanding patterns of anomalous spending. Aggregated insights from this data can aid in refining anomaly detection models or even monetizing insights through partnerships with organizations focused on financial risk assessment.

Why It Belongs to Types 3 and 4:

This query spans Type 3 (Features Analysis) and Type 4 (Benefits from Data):

• Type 3 - Features Analysis: The data provides insights into the performance and effectiveness of the app's anomaly detection system, guiding improvements in fraud detection features, user support protocols, and potentially new security-related functionalities within the app.

• Type 4 - Benefits from Data: This analysis also has value beyond the app itself. Anonymized and aggregated data on anomaly patterns could be shared with or sold to third-party partners interested in financial risk analysis and fraud detection. This offers a potential revenue stream while maintaining user privacy.

Together, these dual purposes make it a Type* question, addressing both internal feature enhancement and external business opportunities.