Combating Claim Fraud: Reduce False Positives with GenAI and Graph

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.

The National Association of Insurance Commissioners (NAIC) states that The Coalition Against Insurance Fraud incurs costs of $308.6 billion annually for businesses and consumers.* (source: These claims predominantly affect property & casualty, automotive, and business insurance sectors. Adding to the complexity, each of the 50 states in the US has its own regulatory body, each with distinct rules and regulations. Some states have enacted legislation targeting various types of insurance fraud schemes, such as agent and broker schemes, underwriting irregularities, vehicle insurance schemes, property schemes, doctor and personal injury schemes, up-charging damages, inflating damage values, salvage fraud, among others. Special Investigations teams are leveraging deeply connected patterns with Neo4J Graph algorithms to explore emerging technologies that reduce settlement times, optimize fraud detection, and enhance claim adjustment efficiency.

Michael Moore, Ph.D., Senior Director of Strategy & Innovation at Neo4j, will join Scott Heath, VP of Fraud at Expero,to demonstrae how to utilize GenAI with Neo4J Graph technology, Human-in-the-loop, and visualization techniques to ensure organizational compliance with Special Investigations units.

This webinar aims to pinpoint areas where Graph DB and Graph Machine Learning, Geospatial, TimeSeries, AI/ML/LLM, Visualization, and Graph technology can enhance Claim fraud identification accuracy by over 45%, and how involving the ‘Human in the Loop’ can preempt your state’s fraud prevention legislation.

Throughout this online meetup, you'll glean insights from our experts on how Neo4J and Expero can unleash your organization's potential.

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.

What You'll Gain:

Master the Complexity of Insurance Fraud: Explore how government regulations and graph link analysis techniques are shaping the future of claim investigations.
Harness Neo4J Graph Analytics: Learn how to implement practical methods for claim fraud identification, complex dependency management, and "human-in-the-loop" collaboration.
The Art of the Possible: Witness live demonstrations showcasing Expero's Connected Platform, a powerful combination of visualization matching, graph analytics, and machine learning, designed to reduce false positives and enhance accuracy.

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