Synthetic identity fraud is the fastest

growing type of fraud1

Traditional models miss 85% of synthetic

identity fraud2

Financial fraud cost $5 trillion

in 20193

85% $5T

Find More Fraud withGraph Data Science

The Growing Problem of Fraud

Learn more about the Neo4j Graph Data Science Library

© 2020 Neo4j. All rights reserved. neo4j.comGet in touch: info@neo4j.com

READ THE WHITEPAPER

Analyze the network structure of your

data

Investigate yourdata

See clusters and outliers

Graph Algorithms Learn from Your Data's Network Structure

CommunityDetection

Centrality(Importance)

Similarity

HeuristicLink Prediction

Pathfindingand Search

Identify disjointed groups that share

identifiers

Measure influence and transaction

volumes

Measure account similarity or fraud

ring similarity

Find unobserved relationships and add them to your

data

Filter transactions with extremely short

paths between people

Run graph algorithms to reveal patterns in your data

Extract graph features from your data

Train your machine learning models using graph features

Achieve greater predictive accuracy from existing data

Gain predictive accuracy

Find emerging fraud

Increase recovery rates

Graph Data Science Unleashes the Power of Your Data

Improve Fraud Detectionwith Graph Data Science

Business Results

1. McKinsey. Synthetic identity theft is fastest growing type of fraud

2. ID Analytics. Traditional models miss 85% of synthetic identity fraud

3. Crowe UK. Financial fraud cost more than $5 trillion globally