Course Schedule

Day 1 - Wednesday 08 December, 2021
Opening Session

Intro, objectives and expectations

Session One (01:00 pm To 02:00 pm)

The Introduction to the problem of Medical Insurance Fraud

  • Medical Insurance Fraud and its implications for society and economies
  • Major Types of Fraud, waste, and abuse in healthcare insurance industry
  • Most common fraud schemes
Break (20 minutes)
Session Two (02:20 pm To 03:20 pm)

Basic Machine Learning Applications in Health Care Fraud Detection – EDA, Reporting & Dashboards

  • The Evidence of Healthcare Fraud and How to Collect it / Discuss what data to analyze
  • What kind of data is available for the health care fraud detection?
  • What kind of EDA is useful in health care fraud detection?
  • What is Data Analytics?
    • Descriptive, Diagnostic, Predictive and Prescriptive
    • Importance of Descriptive & Diagnostic Analytics
    • When we need Predictive & Prescriptive Analytics
  • Fundamentals of Descriptive Analytics – Exploratory Data Analysis (EDA) / BI Reports / Visualization
Break (20 minutes)
Session Three (03:40 pm To 04:40 pm )

Descriptive / Diagnostic Analytics in Action to combat Health Care Fraud

  • Dashboards
    • How to do efficient EDA?
    • How to develop effective and outcome driven dashboards?
  • What KPIs to report?
  • How to convert your FWA expertise into business rules that could be run automatically every day / hour / week? – Case studies and Roundtable discussion
  • Examples of visualization dashboards / tools to help fraud investigation
  • Possible demo of some of these visualization tools / dashboards
Day 2 - Thursday 09 December, 2021
Session One (01:00 pm To 02:00 pm)

Deep Dive on Machine Learning in Health Care Fraud Detection - Case Studies and Round Table Discussions

  • Review of previous day learning sessions and data analytics discussed
  • Fundamentals of Machine Learning - Predictive & Prescriptive Analytics
    • Major types of Machine Learning (e.g., classification, regression, association rules, clustering, text analytics, anomaly / outlier detection, etc) and relevant examples
    • Common use cases where Machine Learning is used but you may not know it
    • Common health care use cases where AI / machine learning is used today
  • Effective feature construction for successful fraud detection using ML / AI
  • How basic AI and machine learning could be used to fight health care FWA?
    • Business rules – quick review from yesterday
    • Classification models – detecting variations of know health care fraud
  • Common challenges and risks using classification predictive models
Break (20 minutes)
Session Two (02:20 pm To 03:20 pm)

Predictive / Prescriptive Analytics in Action to Combat Health Care Fraud

  • How advanced unsupervised AI and machine learning could be used to fight health care FWA?
    • Feature generation for unsupervised learning
    • Clustering approaches for fraud detection
    • Anomaly / Outlier Detection – detecting novel types of health care fraud
    • Link Analysis – detecting fraud rings and nation-wide organized fraud groups
  • Doing your homework – assets, marital and financial status, bankruptcies/divorces/substance abusedata
  • Common challenges and risks using unsupervised ML models for fraud detection
  • AI and machine learning for Post-pay vs. Pre-Pay Fraud Detection
  • Reviewing actual use cases of successful investigations from AI and machine learning
  • Round table discussion on possible fraud use cases and how AI and machine learning could help
Break (20 minutes)
Session Three (03:40 pm To 04:40 pm )

Fraud Use Cases and Open Discussions on how to apply ML and AI to address those

  • Open discussion of the course
  • Each participant will come prepared with their own fraud use case and we will work together to analyze how to address it and how to apply machine learning and AI
  • Quiz and discussion of responses
  • Review of course and wrap up
Course Program
Time Topic
Day 1
12:45 to 13:00Registration & Introduction
Day 1-2
13:00 to 14:00Session One
14:00 to 14:20Break (20 minutes)
14:20 to 15:20Session Two
15:20 to 15:40Break (20 minutes)
15:40 to 16:40Session Three