Fraud Detection and Prevention Analytics

Fraud Detection and Prevention Analytics

Cyber fraud costs organizations billions of dollars each year. Online adversaries are on the rise, as enterprise teams struggle to analyze ever-growing mountains of data, exceeding human capacity to handle. Types of cyber fraud include: financial fraud, with money laundering and merchant fraud; healthcare fraud, with provider fraud and consumer fraud; e-commerce and retail fraud. Using a holistic and risk-based approach, and powered by machine learning, Gurucul Risk Analytics (GRA) provides state-of-the-art capabilities to analyze large volumes of data, and to detect and predict anomalous behavior, that can help prevent large-scale fraud of both internal and external users.

Why Choose Gurucul Fraud Detection and Prevention Analytics?

Using a risk-based approach, and award-winning machine learning algorithms, Gurucul provides best fraud detection of both internal and external users.

Financial Fraud Analytics-IconFinancial Fraud Analytics

Offers analytics for merchant fraud, money laundering, and credit card hacking. Use cases include cyber fraud detection and deterrence, as well as for treasury, accounting, payments, and areas concerning funds transactions. Gurucul anti-money Laundering (AML) models identify patterns of placement, layering and integration. This would include abnormal prices and/or suspicious quantities of product or services being sold to a customer.

Healthcare Claims Fraud Analytics-IconHealthcare Claims Fraud

GRA links data from a multitude of sources to provide a composite view of a patient’s condition, and highlight anomalous transactions, based on historic user and enterprise profiles, and public records.  GRA analyzes and risk scores fraud and abuse which covers provider fraud, including billing irregularities and falsifying serviced data, as well as, consumer fraud, with detecting medical identity theft, false claims and more.

Healthcare Claims Fraud Analytics-IconE-Commerce & Retail Fraud Analytics

Challenges covered by these fraud analytics involve point of sale fraud, or within enterprise environments. These cases include external users in physical retail environments, and internal users accessing data of important customers for personal gain. As well, these analytics manage the overwhelming onslaught of heightened online transaction cycles, such as Cyber Monday, and other seasonal commercial events.

Gurucul-Miner-IconGurucul Miner™

This solution feature offers a contextual search using big data to mine linked users, accounts, structured and unstructured data, along with risk scoring capabilities at the transaction, account and network levels. It also includes the ability to save and export results for reporting.


Gurucul Risk Analytics

Gurucul’s Fraud Detection and Prevention Analytics is built on the GRA core architecture PIBAE (Predictive Identity-Based Behavior Anomaly Engine). It offers a broad array of user and entity behavior analytics (UEBA) and identity analytics (IdA) features, driven by mature machine learning, drawing rich critical context from big data. Its features include:

Industry’s largest library of machine learning algorithms

Most granular and self-tuning risk modeling capabilities

Flexible metadata framework

Signature-less technology

Fuzzy logic-based link analysis

Built to scale using big data foundation



Gurucul Fraud Detection and Prevention Analytics –

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