Defense & Aerospace — Machine Learning Model Development for Fraud Detection

Overview

The primary purpose of this DAG is to construct robust machine learning pipelines that are capable of detecting fraud and anomalies in the Defense and Aerospace industry. The data sources for this workflow include ERP transaction logs, sensor data from aircraft systems, and historical fraud case databases. The ingestion pipeline begins with the extraction of these datasets, followed by data cleansing and feature engineering to create relevant attributes for model training. The processing steps i

The primary purpose of this DAG is to construct robust machine learning pipelines that are capable of detecting fraud and anomalies in the Defense and Aerospace industry. The data sources for this workflow include ERP transaction logs, sensor data from aircraft systems, and historical fraud case databases. The ingestion pipeline begins with the extraction of these datasets, followed by data cleansing and feature engineering to create relevant attributes for model training. The processing steps include model selection, training, and evaluation, where various algorithms are tested to identify the most effective model based on performance metrics such as precision, recall, and F1 score. Additionally, the DAG incorporates batch and online deployment strategies to ensure that models can be utilized in real-time applications. Quality controls are implemented to monitor model performance continuously, with specific KPIs tracked to detect any drift in model accuracy. In the event of performance degradation, an automated retraining process is triggered to refresh the model with new data, ensuring sustained accuracy and reliability. The outputs of this DAG include trained machine learning models, performance reports, and alerts for any detected anomalies. The business value lies in the enhanced ability to prevent fraud, reduce financial losses, and maintain compliance with industry regulations.

Part of the Fraud & Anomaly Analytics solution for the Defense & Aerospace industry.

Use cases

• Enhanced fraud detection capabilities reduce financial losses
• Improved compliance with regulatory standards in Defense & Aerospace
• Increased operational efficiency through automated processes
• Timely alerts for anomalies prevent potential threats
• Data-driven decision-making enhances strategic planning