Fraud Detection and Prevention in Healthcare Claims

• Challenges in payer and provider Fraud
• Methods to reduce false positives by 10%
• Increase accuracy of ID and intervention
• Use of visualizations for explainable ML

Fraud Detection and Prevention in Finance

• Key Challenges in Complex Fraud
• Methods to Reduce False Positives by 10%
• Increase Accuracy of ID and Intervention
• Visualizations for Explainable ML

Fraud Detection Round Table with TigerGraph

• Graph for all Fraud types
• Cut costs by 10% with Graph + ML
• Drive real-time fraud detectiondecisions
• ML approaches combined with Graph

Enterprise Security System In Real-Time Using TigerGraph & Kafka Streams

• Modeling Security Systems with Graph DB
• Propagation in Real Time
• Comprehensive Security Model
• TigerGraph and Kafka

Fraud Detection Using ML & Graph

• Technology integration
• Actor & entity relationships
• Risk scoring with real-time data
• Machine Learning for fraud detection

Patient 360 + Opioid Fraud Detection Using Machine Learning and TigerGraph

• Full Patient 360
• Churn Prevention
• Working Prototypes
• TigerGraph

Decreasing Risk and Enabling Real-Time Decision Making in Alternative Investing

• Financial Alternative Assets
• Portfolio Optimization
• Asset Management
• Compliance & Audit

Fraud Prevention in Financial Services Using Google Cloud Bigtable & Janusgraph

• Finserv Fraud with Graph & ML 101
• Demo: Insurance Fraud
• Demo: Banking Fraud
• GCP - Google BigTable & Graph

Fraud Detection and Optimization using Graph & ML

• Finserv Fraud with Graph and ML 101
• Determanistic Algorithms
• Demo: Insurance Fraud
• Datastax Enterprise

Lock Down Funding for Intelligent, Graph-Enhanced Fraud Solutions

• 6 Steps for Graph Project Funding
• Value Calculation Examples
• ROI Planning
• Get Graph Projects Funded with Neo4j

Graph & Machine Learning for Fraud Detection

• Graph 101 for Fraud Avoidance
• Fraud Machine Learning Algorithms
• Visualizations for Fraud Avoidance
• Demo: Credit Card Banking

Thwart Fraud Using Graph & AI

• Prop Graphs for Fraud
• ML Algorithms for Fraud Avoidance
• Rapid Prototyping for Fraud Apps
• Neo4j & Expero Graph

DataStax Edition: Using Graph Technology to Detect & Prevent Fraud

• Graph for Fraud Algorithms 101
• Fraud Case Study: Financial Services
• Rapid Prototyping for Fraud with Graph
• Datastax & Expero for Fraud

Use Real-time Graph Technology to Detect & Prevent Fraud

• Graph Visuals for Fraud Detection 101
• Risk Fraud Ring Use Case
• UI Toolkit
• Datastax & Expero for Fraud

Fraud Prevention: Healthcare

• Claims Fraud
• Risk Scoring
• Opioid Segmentation
• Fraud Intervention

Fraud: Detection & Avoidance of Opioid Fraud

• Fraud Segmentation
• Care Plan Flagging
• Relationship Graphing
• Geographic & Time Series

Fraud: Detection & Avoidance in Banking

• Transaction Segmentation
• Clustering Algorithms
• Transaction Time Series
• Fraud Intervention

Multitouch: Nuclear Power Plant

• Bi-temporal User Interface
• Time Series Data
• High Performance Computing
• Real Time Data interfacing

Opioid Fraud Detection Using Deep Embedding

Fraud Detection Using Deep Learning

To Catch a Thief… You Need Entity Resolution

Use Real-time Graph Technology to Detect and Prevent Fraud