Alert Extractor
Purpose
Alert Extractor parses raw AML alerts from transaction monitoring systems and converts them into structured JSON data for use by AML compliance analysts. Its purpose is to organize alert information into a consistent output format while identifying extraction status, data quality notes, data gaps, and extracted fields aligned to FINRA 19-18 categories.
Primary users
The primary users are external client AML compliance analysts who need to review raw alert outputs generated by transaction monitoring systems. The agent is designed to support analysts working with AML alert information in varying formats.
Where it fits (process/stage/trigger)
Alert Extractor fits after a transaction monitoring system produces a raw AML alert output and before the information is reviewed or used in a structured compliance analysis workflow. The trigger is the availability of a raw alert in text, XML, JSON, or another provided format.
Key capabilities / workflow
The agent analyzes raw AML alert content, checks whether the format is readable, extracts relevant fields, records data quality notes, identifies data gaps, and generates structured JSON. The extracted fields are organized by FINRA 19-18 categories, including customer information, transactions, securities, counterparties, system flags, and behavioral observations.
Inputs
The input is raw alert output from a transaction monitoring system. The provided alert may appear in varying formats, including text, XML, and JSON.
Outputs / Deliverables
The output is structured JSON containing extraction_status, data_quality_notes, a data_gaps array, and extracted fields organized by FINRA 19-18 categories. These categories include customer information, transactions, securities, counterparties, system flags, and behavioral observations.
Value
Alert Extractor helps transform inconsistent raw AML alert outputs into a structured JSON format that is easier for compliance analysts to review and use. It supports consistency in extraction, highlights data quality issues, and makes data gaps explicit for downstream analysis.
