Operational Excellence: Optimize enterprisewide processes and operations 

System: Business Intelligence Platform, Data Matching Process, Workload Management Tool

Actor: End Users, Data Analysts, Fraud Analysts, Project Managers

Scenario:

Data analysts and fraud analysts in an organization want to replace manual data-matching processes with algorithms that detect anomalies and fraud.

The current manual data-matching processes are time-consuming and prone to errors, limiting the efficiency and effectiveness of fraud detection.

Data analysts and fraud analysts work together to develop and implement algorithms that can automatically identify anomalies and potential fraud patterns based on predefined rules and statistical models.

By deploying automated data-matching algorithms, the organization can improve the accuracy and speed of fraud detection, enabling timely mitigation of risks.

Use Case

Use Case Name: Replace Manual Data-Matching Processes Using Algorithms that Detect Anomalies and Fraud

Primary Actor: Data Analysts, Fraud Analysts

Goal: To replace manual data-matching processes with algorithms that detect anomalies and fraud.

Pre-conditions: Manual data-matching processes for fraud detection are in place.

Post-conditions: Automated data-matching algorithms are deployed, improving the accuracy and speed of fraud detection processes.