FWA, AI, Machine Learning, and Local Trends.

• GCC v Western Health Care Systems
• Most common fraud schemes in US and internationally
• Examples of FWA experience in GCC
• UAE Regulations for Medical Audit & Recovery
• Usual Prevention & Detection and Investigation measures used in UAE and GCC region
• How local regulations can impact off the shelf analytics
• Ways Machine Learning will adapt your future analytics
• Current local trends
• How AI can provide insights to address shifting local trends
• Group Case Study - based on a real-life experience

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

Descriptive / Diagnostic Analytics in Action to Combat Health Care Fraud

• Visualization Dashboards
  • How to do efficient EDA?
  • How to develop effective and outcome driven dashboards?
  • What skills to build?
  • What tools / vendors to consider?
• 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

Visualization Dashboards / Tools

• Building business rules in practice – real world example with realistic data
• Simple examples of visualization dashboards / tools to help fraud investigation
• Quick exercise in building a dashboard
• Possible demo of some of these visualization tools / dashboards

Wrap up discussion

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

• 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 e AI / machine learnings used today
• Bringing data to your environment (data ingestion) and data preparation (data wrangling / preprocessing)
• 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 previous day
  • Classification models – detecting variations of know health care fraud
• Common challenges and risks using classification predictive models

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 abuse data
• 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

Reviewing Use Cases of Investigations and Round Table Discussion on Possible Use Cases

• What’s next after you build you data analytics solution? Deploying the analytics solutions
• What it takes to successfully adopt your analytics solution?
• 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

Open Group Discussions / Analyzing How to Address and Apply ML & AI

• 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

Closing, Certificate Distribution, and End of Course!