Specially Tuned Machine Learning Models Applied to Big Data Can Identify and Alert on Industry Specific Threats in Finance, Healthcare and Retail
May 14, 2018 09:01 AM Eastern Daylight Time
LOS ANGELES–( BUSINESS WIRE)- Gurucul, a leader in behavior based security analytics and intelligence technology for on-premises and the cloud, today announced Gurucul Fraud Analytics, which uses purpose-built machine learning models to correlate cross-channel behaviors and detect suspicious activities associated with fraud in financial, healthcare and retail environments.
|.@Gurucul #fraudanalytics solution monitors user and entity behaviors across multiple channels using specially tuned #machinelearning models to prevent specific threats in finance, healthcare and retail||Unlike traditional siloed, rule and pattern-based transactional solutions, Gurucul offers a real-time behavior based fraud analytics solution that uses open choice of Big Data to store cross-channel transactions, contextual metadata and run machine learning models that provide actionable risk-scored intelligence on suspicious activity.|
360 Degree View of Fraud
Gurucul Fraud Analytics links data from PoS devices, endpoint workstations, mobile devices, web, voice, servers, IoT devices, etc. with users and/or entities to build a 360-degree contextual view of transactions. Meanwhile, pre-packaged and tuned machine learning models (unsupervised, supervised and deep learning) are applied to predict and detect industry specific fraud use cases. Customers can also customize existing models or build their own from existing templates using Gurucul Studio™.
The Gurucul Fraud Analytics risk engine continuously scores user and/or entity activity against historical as well as dynamically created peer group behavior to detect anomalies and generate risk prioritized alerts for further investigation. These risk scores can also be used by applications to enforce security policies and make real-time business decisions to stop fraud before it occurs, such as introducing step-up authentication challenges.
Industry-Specific Use Case Detection
Some of the pre-packaged industry focused fraud use cases supported out-of-the-box by Gurucul Fraud Analytics include:
- Financial Fraud: Money laundering, credit card fraud, identity fraud, account takeover, trade surveillance, mortgage fraud, etc.
- Healthcare & Claims Fraud: Prescription fraud, patient data breaches, claims fabrication, upcoding, unbundling, etc.
- E-commerce & Retail Fraud: Charge-back, promotion abuse, mass registrations, unauthorized discounts, unauthorized sale voiding, exceptions, returns, etc.
“Digital transformation initiatives in virtually every industry are generating terabytes of data and increasing transaction complexity, which makes it impossible to monitor for fraud using human resources, or even rule and pattern-based detection solutions,” said Nilesh Dherange, CTO of Gurucul. “Gurucul Fraud Analytics’ open support for Big Data infrastructures, ability to build cross-channel context and apply advanced behavior analytics is tailor-made for detecting fraudulent activities in transaction-intensive environments and applications including financial, healthcare, e-commerce, retail, hi-tech and more.”
For streamlined investigations and case management, Gurucul Fraud Analytics’ intuitive and customizable dashboards provide a holistic view of the fraud landscape with visualizations that significantly reduce incident analysis and auditing for forensics. Gurucul also supports out-of-the-box API integrations with most applications including trading, chat, ticketing or case management, point of sale video integration, telephony systems, enterprise fraud platforms and more, to automate fraud operations and response workflows.
Gurucul Fraud Analytics is available immediately from Gurucul and its business partners worldwide. It can be deployed on-premise or in the Cloud with a managed service option.
About Gurucul Risk Analytics
Gurucul Risk Analytics (GRA) is a multi-use security analytics platform with an open architecture. It supports a choice of big data for scale, can ingest virtually any dataset for desired attributes and includes configurable prepackaged analytics. In addition, Gurucul Studio enables customers to create custom machine learning models to meet unique requirements without coding and minimal data science knowledge. GRA ingests and analyzes huge volumes of data generated when users access and interact with business applications, in both the data center and the cloud, to generate risk scores, identify security threats and prevent data breaches. The Gurucul platform has been successfully deployed by several government agencies and Global Fortune 500 companies.
Gurucul is a global cyber security company that is changing the way organizations protect their most valuable assets, data and information from insider and external threats both on-premises and in the cloud. Gurucul’s real-time security analytics and intelligence technology combines machine learning behavior profiling with predictive risk-scoring algorithms to predict, detect and prevent breaches, fraud and insider threats. Gurucul technology is used by Global 1000 companies and government agencies to fight cyber fraud, IP theft and account compromise. The company is based in Los Angeles. To learn more, visit gurucul.com and follow us on LinkedIn and Twitter.
Marc Gendron PR for Gurucul