Insurance providers are now caught in a unique market and business dynamic: the goal of driving new revenue and maximizing customer satisfaction, while avoiding waste and fraud. With optimization efforts focused on increasing revenue streams, insurance companies are lagging behind in cost minimization, specifically in the domains of fraud, waste, and persistent financial loss. Outdated, labor-intensive methods based on hard-coded procedures are not keeping up with complex rules changes in the insurance industry and increasingly sophisticated fraud schemes.
New methods and technologies, such as embedding ML models in the adjudication process, enable automated prevention of improper disbursement of funds. Additionally, these methods dramatically increase the success of post-adjudication claw-backs by accurately identifying claims with high expected payment recovery. All insurance lines of business, including Healthcare, PNC, Auto, Life, and others, are utilizing the combination of Machine Learning, Knowledge Graph technology, and visualization to implement real-time intervention strategies. The focus of this webinar is to identify how these technological advances can directly increase the accuracy and shorten process time for ‘Human in the Loop.'
This event is designed as a 'Speed Dating' format focusing on key topics for under 15 minutes in order to maximize insights. During this online meetup, you will learn from our experts how Tarleton State University’s Tarleton Analytics Institute (TAI) can unlock the potential in your organization. We will feature unique TAI business, ML & Visualization technology lightning talks, followed by a short Q&A session.
Key Learning Topics:
- What Are the Key Challenges in Payer and Provider insurance - Illustrate why Visualization, ML & Graph still utilize ‘human in the loop’ for maximum accuracy and productivity
- Methods to Reduce False Positives and Reduce Improper Claim Payments - Review ML & knowledge graph combination techniques to reduce false positive signals and reduce improper claim payments
- Increase accuracy of current ML Systems - Strengthen and increase accuracy for Fraud, Waste and Abuse identification with combinations of technique and technologies
- Creation of Preventive & Predictive Analytics - Access to different roles from customer success, new sales, fraud prevention and others
- Use of Visualization for ‘Explainable’ ML - Show practical uses and methods for fraud identification, complex dependency and case management