Fraud and loss affect healthcare payers and providers at a staggering amount each year. This also includes all areas of the business from underwriting to the investigations of claims and payments. In addition, fraud and loss is time consuming to investigate and fraudsters become more sophisticated and utilize more complex methods and technology that make it even harder to detect. The sophistication and pressure from world health events has driven the need for real-time analytics and prevent and intervene strategies.
The focus of this webinar is to identify how Machine Learning, Visualizations, and new technology like Graph can directly increase the accuracy and output of systems and include 'human-in-the-loop' to get ahead of fraud. This event is designed in a Speed Dating format with a focus on key topics for under 15 minutes in order to maximize the insights. During this online meetup, you'll learn from our experts on how Expero can unlock the potential in your organization. We will feature unique Expero lightning talks followed by a short Q&A session.
Key Learning Topics:
- Key challenges in payer and provider complex Fraud: illustrate why Machine Learning, Visualizations, and Graph technology utilize 'human-in-the-loop' for maximum accuracy and productivity
- Methods to reduce false positives by 10%: review ML combination techniques with Graph and other platforms to reduce false positive signals
- Increase accuracy of Fraud identification and intervention: strengthen and increase accuracy with combinations of technique and technologies
- Creation of Fraud based 'data products' for preventive and predictive analytics: access to different roles from fraud management, investigators to data and analytics teams
- Use of visualizations for explainable ML: show practical uses and methods for fraud identification, complex dependency, and case management
Session 1 (10 min): Why and How ML, Visualizations, and Graph Technology Combined with 'Human-In-The-Loop' are Game Changers for Fraud Detection, Investigation, and Prevention
Session 2 (20 min): Use Visualizations to Identify Human Interaction for Fraud - 'The Art of the Possible' Fraud Workflow Demonstrations, Management, Investigations, ML, and Data Practitioner Views
Session 3 (20 min): Details on How ML and Combinations of Technology Can Reduce False Positives, Increase Accuracy, and Move Towards Predictive Forecasting and Active Scenario Analysis
Session 4 (5 min): Tie Together the Elements of ML, Visualizations, and Graph Technology for a cohesive approach to Fraud Identification, Analysis, and Prevention
Session 5 (5 min) Open Q&A