Identify patterns across multiple fraud detection algorithms

We need to functionality that automatically identifies and correlates patterns across our existing fraud detection algorithms to provide comprehensive fraud analysis capabilities.

Our current setup includes 23+ fraud detection algorithms, and we want to analyze patterns across all of them simultaneously. The system needs to support multiple data sources including SDK data analysis, Google API data integration, and client-provided data correlation.

The core functionality should include:

• Automatic pattern recognition across algorithms

• Cross-referencing of fraud indicators

• Correlation of related fraud patterns

• Statistical analysis of pattern significance

• Identification of compound fraud scenarios

We're looking to successfully identify patterns that span multiple fraud detection algorithms while reducing manual analysis time and maintaining accuracy compared to our current manual processes. The end result should generate comprehensive fraud pattern reports.

Based on our discussion, we think this breaks down into several features we'd build in sequence:

First, we need the ability to load CSV files and automatically label data based on column names, with a 1000 unique values per column limit rather than our current 1000 row CSV limit. We'd load the CSV into a data warehouse, group data to determine unique column values, and add those as labels with relationship mapping.

Next, we want to write questions referencing labels that are columns. The system would generate traditional BI queries correlating those columns, then feed results to our LLM to generate story tiles. For example, we'd want to ask: "For customer @customer_column_value, generate a table comparing total queries on two axes - device battery level and screen brightness. If queries are evenly distributed, output 'no fraud detected.' If there's significant correlation, output 'possible fraud detected' and render a heat map."

We'd also want users to author dashboards as grids of components, where each component displays results from text questions and can take labels as inputs for parameterization across multiple queries.

Eventually we want to hook up charting APIs for heatmap display and set up automated actions to update story views daily and write results to Google Drive.