Fight Fraud with TigerGraph

Fraud is a growing problem in all industries, with eCommerce losses alone exceeding $57.8 billion in 2017. For every dollar involved in fraudulent transactions, the financial services industry spends $2.67 dealing with chargebacks, fees, interest, and labor.

Fraudsters are getting more sophisticated over time, creating a network of synthetic identities combining legitimate information like social security or national identification number, name, phone number, and physical address. Legacy fraud detection systems are largely based on the analysis of the behavior of an individual business entity, such as a customer, citizen, device, doctor, or healthcare provider, and find unusual patterns in that behavior.

Fraud is perpetrated using fake accounts created with synthetic identities. Each individual fraud account looks and behaves much like a legitimate account, making detection much harder with traditional solutions. Fraud detection using graph database technology requires going beyond individual account behavior, analyzing relationships among groups of accounts or entities over time, while often combining information from third party sources. Traditional fraud solutions built on relational databases were not designed to address this challenge, which is why organizations are turning to graph database for fraud detection capabilities.

Graph fraud detection excels in scenarios like healthcare fraud related to opioid addiction treatment centers. Take this graph database fraud detection example: A provider is prescribing a large number of patients to one specific opioid or drug addiction treatment center. Traditional fraud solutions can’t find anything unusual based on the information available for the doctor as well as the treatment center.

TigerGraph’s graph analytics fraud detection capabilities tap into the enterprise knowledge graph from a third-party source — such as Thomson Reuters or Dunn & Bradstreet — to find all known administrators for the drug treatment center and their current and previous addresses listed in the public domain. One of the previous addresses for the administrator is very close to the address for the physician prescribing patients to the drug treatment center.

In order to detect this hidden relationship between the doctor and the administrator, TigerGraph executes a query that goes eight hops across claims for patient visits and opioid treatment center claims by the same patients. Combining that with the third-party knowledge repository data such as addresses and phone numbers reveals the collusion. Traditional fraud solutions built on relational databases struggle to incorporate new data sources from third parties due to rigid schema and require computationally intensive database joins, rendering deep link analysis infeasible. This is where a fraud detection graph database provides clear advantages.

Fraud detection graph technology is time-sensitive: every passing minute, hour, and day that fraud goes undetected results in increasing losses for your organization as well as for your customers or citizens. TigerGraph stands as the top fraud detection graph database, purpose-built for real-time fraud detection to address this challenge. Our fraud prevention database capabilities ensure rapid response to emerging threats.

Less than 1% of the total call volume for telecom or claims data for healthcare and government benefits or payment transactions for financial services are fraudulent. This means that the machine learning models do not have sufficient training data with confirmed fraud activity to learn and improve their accuracy.

TigerGraph’s native parallel graph database is purpose-built to address this challenge, establishing itself as the top graph database for fraud detection. Our platform combines the power of fraud detection graph database technology with advanced machine learning algorithms to deliver superior accuracy and performance in identifying fraudulent activities across complex relationship networks.