Morton Analytics LLC is a data analytics firm specializing in providing actionable insights for healthcare providers and payers. Known for its expertise in handling large datasets and delivering strategic solutions, the company excels in transforming complex data into digestible, actionable information. However, the challenge lies in the efficient detection of fraud, waste, and abuse (FWA) within the vast volumes of Medicare claims data. This is a nuanced problem that requires not only technical prowess but also domain-specific knowledge of healthcare billing practices.

The current gap in FWA detection impacts the company by potentially allowing significant financial losses due to undetected fraudulent activities. The lack of a sophisticated, automated detection system means that Morton Analytics cannot fully leverage its data analytics capabilities to provide comprehensive solutions to its clients. This project offers students the opportunity to bridge this gap by developing a cutting-edge FWA triage tool that automates the detection process and enhances the company's service offerings.

This project unfolds in several phases: data processing and pipeline creation, rule-based detection implementation, AI integration for complex case analysis, dashboard development, and business case presentation. Each phase presents unique challenges and learning opportunities. Students will learn to manage real-world data volumes, apply machine learning techniques, and develop user-friendly interfaces, all while maintaining a focus on delivering business value.

This project is ideal for students because it combines technical, analytical, and business skills, providing a holistic learning experience that mirrors real-world professional scenarios. By tackling this project, students will develop critical skills in data analytics, project management, and solution development, all of which are highly valuable in careers such as consulting, finance, operations, and healthcare policy. The project is exciting and relevant because it directly impacts the efficiency and effectiveness of healthcare fraud detection, a field with significant financial and ethical implications.