Day 1 - Wednesday 08 December, 2021
Opening Session
Kick-off
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