Financial Anomaly Detection
Last updated
This project demonstrates how to use Gaio DataOS to build an application that detects financial anomalies based on given financial transaction information.
Using this application, users are able to:
Import financial transaction data
Detect, classify, and view analytics
Insights regarding financial anomalies within their transactions.
Connects to ClickHouse Cloud or imports CSV
Uses builder tasks in Gaio to:
Import data
Clean and transform data
The import process ends in the flat table tb_financial_anomaly
Detection logic is split into SQL builders to identify:
Large Expense
Duplicate Transaction
After Hours Transaction
Repeat Reimbursement
Revenue Spike
Refund Surge
Each detected anomaly receives:
A severity score
A unique anomaly_id
Enriched metadata
💡 Note: If multiple anomalies occur in the same transaction, each one receives a different
anomaly_id.
Final consolidation into: detected_anomalies
Uses the detected_anomalies and anomaly_classification_history tables.
Analytics like count of anomalies, number of each classification type, and distribution of anomaly severity are created using Report tasks and displayed on the dashboard
A set of filters is created using the Form task to allow a user to filter by date, transaction type, sector, category, and payment type
There is also a flat table that shows most recent classifications and a table showing all anomalies that need to be classified
Using the anomalies awaiting classification table, users can classify an anomaly or view more specific anomaly details by clicking on its ID.
By clicking the leftmost icon in the anomalies awaiting classification table, users can classify an anomaly using the form task
Some fields in the form will auto-complete with information from the chosen anomaly using parameters
When the Form is submitted, the Update Anomaly Classification process will run, this process is comprised of two major parts:
Update task edits the current anomaly row in the detected_anomalies table
Incremental builder task adds a new row with the anomaly and classification to the anomaly_classification_history table
Clicking an anomaly_id opens a dedicated dashboard
Tables used:
detected_anomalies
tb_financial_anomaly
Uses parameters for dynamic filtering
Includes:
Risk level card
Insights based on anomaly type
Charts:
Line chart (monthly averages)
Bar (by sector, category)
Pie (by customer/vendor)
Transaction history table
Styled using HTML & CSS
Two dedicated dashboards using detected_anomalies
Show:
Confirmed anomalies
Pending anomalies
Built with Report Task
Visual ETL workflows in Gaio DataOS
Temporary and Final Tables for data versioning
Report and Content tasks to create adaptive, visually interesting dashboards
Dynamic Dashboards with filters, graphs, and tables
HTML and CSS for stylized and user-responsive dashboard elements
Form Task to allow for anomaly classification without leaving the dashboard
Parameters for data pass through enabling easy navigation of the dashboard
Faster development of anomaly detection logic
Centralized monitoring dashboards
Better financial control and audit tracking
Secure access via permissioned users/groups
Last updated