The Evolving Fraud Landscape

• Set the stage for a hands-on exploration of AI's practical application.

The Power of Data Visualization – From Dashboards to Generative AI for EDA

• What is Data Visualization?
• The journey from traditional static reports to interactive dashboards.
• Leveraging Generative AI for Exploratory Data Analysis (EDA) and Visualization:
• Hands-on Exercise: Generative AI for Visualizing Healthcare Claims Data
  • Task 1: Basic Claims Overview
  • Task 2: Spotting Suspicious Patterns (without coding): Guided prompts to help identify basic fraud schemes
  • Outcome: Experience how quickly you can generate insightful visualizations and spot "interesting patterns" without writing a single line of code.

Understanding Analytics: From Descriptive to Generative
The Spectrum of Analytics: Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, Generative Analytics

Basics of Predictive/Prescriptive Analytics (Machine Learning)

• Introduction to Machine Learning for Fraud Detection - Key ML Concepts for Beginners (classification, anomaly detection, clustering, graph analysis)
• Hands-on Exercise: Building a Simple ML Model (using a simple tool)
• Transition to Healthcare Fraud: Briefly discuss how these basic concepts scale to real-world healthcare fraud data.

Diving into Generative AI, LLMs, and Agentic AI

• Generative AI Demystified – What it is?
• Understanding Large Language Models (LLMs)
• Introduction to Agentic AI/AI Agents
• Demonstrating LLM Capabilities for Healthcare Fraud (using popular LLMs such as GPT, Gemini, Claude, Grok, DeepSeek)
  • Scenario 1: Medical Record Comprehension
  • Scenario 2: Claim Verification
  • Scenario 3: Suggesting Appropriate Diagnosis/Procedure Codes
  • Discussion: Emphasize the potential for investigators to quickly get context and initial verification, saving significant manual review time.

Applications of ML/AI/GenAI to Healthcare Fraud Detection (Deep Dive)

• Visualizing Fraud Schemes without Coding (Revisited and Expanded)
• Advanced Visualization Techniques: Show how to visualize complex fraud schemes using intuitive queries (e.g., Phantom Billing/Services Not Rendered, Upcoding, Unbundling, Patient Roaming/Doctor Shopping Discussion)

Reinforce the concepts of visualization, basic ML, and initial GenAI applications.

Generating Business Rules & Data Preparation for AI Models

• Generating Simple Business Rules using Generative AI – concept creation, tool selection, creating and running the rules
• Essential Steps for Applying AI Technologies:
  • Data Ingestion
  • Data Preparation (Data Wrangling/Preprocessing):
    • Cleaning data
    • Feature Engineering
    • Transforming Data from Claim Level to Provider/Patient Level

Building ML Models with Generative AI Assistance

• Building Machine Learning Models Using Generative AI Technology – demo showing concept creation, tool selection, building the model and running the model
• Model Interpretation and Explainability

Automating Fraud Detection with Agentic AI & Practical Schemes

• Automating Fraud Detection Activities with Agentic AI - demo showing concept creation and Illustrative Workflow
  • Walk Through Several Fraud Detection Schemes (e.g., Durable Medical Equipment (DME) Fraud, Opioid Diversion/Over-Prescribing, Kickbacks & Collusion)

Emerging Trends & The Future of Healthcare Fraud Detection

• The Shift to Pre-Pay Fraud Detection
• The Future Investigator – Enhanced by AI
• Emerging Trends in Healthcare AI
• Key Considerations - Compliance and Governance

• Roadmap for AI Adoption in Fraud Detection
• Open Group Discussion: Applying Learnings to Your Context
• Your AI Journey Continues: Beyond This Course

• Final questions and discussion
• Course evaluation and feedback