Fight Cyber Crime and Fraud with Graph and ML

• Unique Challenges: FinCen reporting and other impacts on compliance
• Tech Innovations: utilizing watch list data & breech data in real time
• Cyber Teams: create quicker outcomes for Fraud Managers, Investigators, etc
• ML & AI: practical methods for higher accuracy & process streamlining

Fight Fraud With Graph + ML

• Key features and issues facing AML - Treasury Changes & Impact
• What will the role of ML, Graph, and Humans be?
• How does TigerGraph address these issues? Demos of the Art of the Possible

Fight Cyber Crime and Fraud With Graph & ML

• Cyber challenges. Why change, and why now?
• Illustrations of how Graph & ML can increase accuracy & intervention
• How simple constructs enable deep, multi-dependency analyses

Financial Services: Fighting AML and Trade Fraud

• 2021 Trading based AML Complexity - Overview of SEC based Trade Compliance
• Art of the Possible: Process improvement, provide alerts, & case management
• How ML & Graph Can Help to Streamline Processes and Improve Accuracy
• What Does New Technology Bring to the Party?

Insurance & Claims Fraud with Graph & ML Round Table

• Complexity and Issues in Fraud Prevention: State & Federal Rules
• Better Investigation Capability: Demos of ML, Graph, and Human-in-the-Loop
• Key AI & ML for Special Investigations and Fraud Prevention Units
• How ML techniques and other methods create more accurate claims prevention

Anti-Money Laundering With FIBO, Graph, & ML

• Overview of AML mandates and process changes impacting audit and compliance
• How to use 3rd party data and Graph & ML for case management efficiency
• AI & ML use cases for fraud investigation with ‘human-in-the-loop’
• How graph improves entity resolution, and scoring and analytics processes

2021 Trends in Fraud (AML)

• 2021 in AML: Anti-Money Laundering Act of 2020 & other AML process changes
• Resources available for AML & how institutions are preparing for 2021
• Key AI & ML for AML: Implementing practical methods of fraud identification
• How new technologies aid in reducing false positives & increasing accuracy

Detecting Fraud with Streaming Data with Imply

• Why fraud use cases require real time, ML, and graph technology. Why Imply?
• Discuss new approaches to fraud detection with workflow demonstrations
• How Imply's platform can be applied to new approaches of fraud detection
• Approach fraud detection using ML, Visualization, and Imply Technology

Insurance Claims Roundtable With TigerGraph

• How ML & Visualizations and 'Human-in-the-loop' create game changers
• 'The Art of the Possible' C360 Demos, Churn prevention, Fraud Workflow
• How ML and technology can reduce false positives & increase accuracy
• Combine ML, Visualization & Technology to approach Fraud analysis

Retail Fraud Round Table with TigerGraph

• Key Challenges in Retail Fraud
• Methods to reduce false positives by 10%
• Fraud based ‘data products’ for Preventive & Predictive analytics
• Use of Visualization for ‘Explainable’ ML

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

Speed Dating with Cassandra

• noSQL, Analytics, Search & Graph
• DSE Studio demo: 3 simult. Cloud vendors
• Demo: Supply Chain, Customer 360 & Fraud
• ER solved with DSE replication

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

Financial Services: Fighting AML and Trade Fraud

• Process improvement
• Provide alerts
• Case management

Fraud Toolkit in Medical Healthcare Claims

• Medical Healthcare Claims
• Machine Learning, Graph Technology, and Time Series Structure
• View Connections to Risky Communities
• Tweak Graph and Machine Learning Algorithms to Flag Identified Patterns

Fraud: Detection & Avoidance of Opioid Fraud

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

Fraud Prevention: Healthcare

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

Multitouch: Nuclear Power Plant

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

Fraud: Detection & Avoidance in Banking

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

Financial Crimes Investigation Workbench

• Financial Crime
• ML & Graph Technology
• Investigation Workbench
• Deep Link Analytics

Opioid Fraud Detection Using Deep Embedding

Using deep machine learning recommender system technology, Expero detects health care opioid fraud.

Fraud Detection Using Deep Learning

Money laundering and credit card fraud detection by graph machine learning.

To Catch a Thief… You Need Entity Resolution

Machine learning entity resolution deduplication of FBI criminal records using supervised learning logistic regression and unsupervised learning clustering.

Use Real-time Graph Technology to Detect and Prevent Fraud

Learn how Graph Technology can help to identify risk and fraud patterns in order to quickly respond. Many new fraud rings use sophisticated measures for credit card and other methods of fraud. Utilizing Graph technology will allow you to see beyond individual data points and uncover difficult-to-detect patterns. Join us to learn how to maximize time and resources with Graph technology vs. traditional relational database platforms.

Actimize Complement

• Learn how Expero's fraud detection system integrates with Actimize
• Understand Expero's graph detection methodologies
• How the Expero system uses graph machine learning methods to view interactions
• Fraud analytics from Actimize AML output in Expero UI