Combating Insurance Fraud with Cutting-Edge Technology
Insurance fraud costs businesses and consumers billions annually, impacting property & casualty, auto, and business insurance. This complexity is furthered by varying regulations across all 50 U.S. states.
Join us for a fast-paced, informative webinar designed to equip you with the latest strategies to combat fraud.
Key Focus Areas:
- Identifying Fraudulent Claims: Discover how Graph Databases (Graph DB), Graph Machine Learning (GML), and AI/ML/Large Language Models (LLMs) can boost claim fraud identification accuracy by over 45%.
- Staying Ahead of Regulations: Learn how "Human-in-the-Loop" techniques ensure compliance with evolving state-level fraud prevention legislation.
- Optimizing Investigations: Explore how Neo4J Graph technology, combined with visualization tools and machine learning from Expero, empowers investigators with faster, more efficient claim processing.
Key Learning Objectives:
- Challenges in Claim Fraud Investigations: Delve into emerging threats, audit & compliance concerns, and investigate best practices for combating claim abuse.
- Unlocking Technological Innovation: Understand why fraud investigators should leverage Neo4J with advanced AI, ML, graph algorithms, and LLM models to minimize false positives and enhance accuracy.
- Empowering Investigators with Next-Gen Tools: Discover how visualization technologies and human-centric processes streamline workflows for fraud management, investigators, and data analysis teams.
- Harnessing the Power of Explainable AI: Learn practical approaches to utilize AI, time-series data, spatial analytics, and ML/Graph algorithms (including LLMs) to empower non-technical investigators as "humans-in-the-loop" for improved accuracy and streamlined processes.
