Insurance payers and providers are now caught in a unique market and business dynamic. The goal of driving new revenue, avoiding internal waste & fraud while trying to maximize customer satisfaction. This set of unique challenges includes all areas of the business from sales, marketing, policy underwriting to the investigations of claims fraud and payments. As insurance companies find new revenue and optimize for better profits, Fraud & Loss contribute to higher premiums. All insurance lines of business : Healthcare, PNC, Auto, Life and others are utilizing the combination of Machine Learning, Graph & Visualization to drive more revenue, cut costs and for actionable real-time analytics, and ‘real time’ prevent and intervene strategis.
The focus of this webinar is to identify how Machine Learning, Visualization and new technology like Graph can directly increase the accuracy and shorten process time for ‘Human in the Loop’. This event is designed as 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 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 by 10% - Review ML combination techniques with Graph and other platforms to reduce false positive signals
- Increase accuracy of current ML systems - Strengthen and increase accuracy for Cross Sell, Churn prevention, Fraud identification with combinations of technique and technologies
- Creation of based ‘data products’ for 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
Insurance payers and providers are now caught in a unique market and business dynamic. The goal of driving new revenue, avoiding internal waste & fraud while trying to maximize customer satisfaction. This set of unique challenges includes all areas of the business from sales, marketing, policy underwriting to the investigations of claims fraud and payments. As insurance companies find new revenue and optimize for better profits, Fraud & Loss contribute to higher premiums. All insurance lines of business : Healthcare, PNC, Auto, Life and others are utilizing the combination of Machine Learning, Graph & Visualization to drive more revenue, cut costs and for actionable real-time analytics, and ‘real time’ prevent and intervene strategis.
The focus of this webinar is to identify how Machine Learning, Visualization and new technology like Graph can directly increase the accuracy and shorten process time for ‘Human in the Loop’. This event is designed as 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 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 by 10% - Review ML combination techniques with Graph and other platforms to reduce false positive signals
- Increase accuracy of current ML systems - Strengthen and increase accuracy for Cross Sell, Churn prevention, Fraud identification with combinations of technique and technologies
- Creation of based ‘data products’ for 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
