WEBINAR: HEALTHCARE FRAUD DETECTION USING AI AND MACHINE LEARNING

October 20-21, 2021, 12:00 PM - 3:40 PM, Dubai Standard Time (GMT+4)

Training Objectives

Completion of this course will provide attendees with a basic understanding of main AI / machine learning methods that could be used for detecting healthcare fraud ranging from developing visualization dashboards for faster and more efficient fraud investigation to creating more advanced predictive models for automated detection of various types of fraud.

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Instructor of this course

Aleksandar Lazarevic

Phd, Machine Learning
VP, Advanced Data Analytics & Data Engineering, Stanley Black & Decker (SBD)
Former Director Data Sciences, Aetna
PhD, Machine Learning, Temple University
MBA (ABD), Carnegie Melon University
MS in Computer Science & Engineering, University of Belgrade

More Detail

This course will provide a basic overview of healthcare fraud and its detection. It will start with covering main fraud schemes exploited globally, follow with providing a comprehensive introduction to the basic components of healthcare data and finish with explaining how AI and Machine Learning can be used to identify potential fraud, waste and abuse in the public and private payer systems.

The software tools and different AI / machine learning methods will be reviewed as well as the newest fraud detection methods based on deep learning. Proactive algorithms used to identify real time large investigations will be reviewed with case studies examined.

Completion of this course will provide attendees with a basic understanding of main AI / machine learning methods that could be used for detecting healthcare fraud ranging from developing visualization dashboards for faster and more efficient fraud investigation to creating more advanced predictive models for automated detection of various types of fraud.

Investigators and Auditors in the Healthcare domain
Certified Fraud Examiners (CFE)
Certified Information Systems Auditors (CISA)
Insurance Claims Adjudicators
Special Investigator Unit Directors/Auditors
Medical Claims Auditors
Attorneys
Third Party Administrators
Professionals who want to expand their skillsets in interviewing techniques
Fraud Analysts
Compliance analysts and managers
Internal and external auditors
Medical audit/claims
Consultants
Those attendees that are new to the fraud and abuse dataanalytics world

  • Group discussions focused on real-world case studies
  • Use of videos, images and brainstorming techniques for
  • quality learning
  • Online quizzes
  • Daily feedback of previous day course activities
  • Live exercises for each attendee

Course Fee $360/Participant

Individual Fee & Discount
25% discount till Aug 26, 2021 to pay $270/participant
15% discount After Aug 26 till Sep 26, 2021 to pay $306/participant
After Sep 26, 2021 to pay $360/participant

Exclusive Group Offers
2+1 Offer: 3 participants for $720
3+2 Offer: 5 participants for $1080


WEBINAR: HEALTHCARE FRAUD DETECTION USING AI AND MACHINE LEARNING - Course Schedule

Day 1 - Wednesday 20 October, 2021
Opening Session

Kick-off
Intro, objectives and expectations

Session One (12:00 pm To 01:00 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 (01:20 pm To 02:20 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 (02:40 pm To 03:40 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 21 October, 2021
Session One (12:00 pm To 01:00 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 (01:20 pm To 02:20 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 (02:40 pm To 03:40 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
11:45 to 12:00Registration & Introduction
Day 1-2
12:00 to 13:00Session One
13:00 to 13:20Break (20 minutes)
13:20 to 14:20Session Two
14:20 to 14:40Break (20 minutes)
14:40 to 15:40Session Three