Fraud Analytics

Monitor Cross-Channel Transactions and Identify Risky Events in Real-Time

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Predict, Detect and Prevent Fraud

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.

Gurucul Fraud Analytics provides a holistic risk-based approach for fraud detection of both internal and external users, using award-winning machine learning algorithms and an open big data architecture. Its data science architecture creates a unique risk score for each internal user, customer or provider entity, using context-driven sensors from public and private data and transactions. It ingests both structured and unstructured data and aggregates risk context for intelligent predictive fraud detection.

Gurucul Fraud Analytics can link data from a multitude of sources to provide a contextual view, and highlight anomalous transactions, based on historic user and community profiles. Its analyzes online and offline activity: public records, contact center interactions, point of sale transactions and ATM transactions. Gurucul Fraud Analytics mines and normalizes data, and then creates a risk score for fraud and abuse. It’s used for real-time decision making or batch scoring of an event. It can also provide scores and risk factors for other systems to use in a decision.

Gurucul Fraud Analytics Pre-packaged Industry Solutions

Gurucul Fraud Analytics Core Capabilities

Fraud Prevention and Detection

Provides user and entity centric (PoS, end point devices, servers, etc.) behavior analytics using 1000+ machine learning models, pre-packaged and tuned to predict and detect industry specific fraud use cases. Allows customization of existing models or build your own fraud models using templates.

Real-time Alerting & Risk Scoring

Provides real-time analytics to detect risky abnormal behavior and send alerts via multiple delivery mechanisms. Leverages a comprehensive risk engine which performs continuous risk scoring based on historical and current behavior. The dynamic risk score can be leveraged by applications to enforce policies and make real-time business decisions.

Investigation and Case Management

Offers comprehensive case management, out-of-the- box customizable dashboards and simple natural language based contextual search capability, providing a single pane of glass for end to end investigations. Allows the ability to provide feedback to the machine learning models based on the investigation findings.

Integration with External Applications

Comes with out-of-the-box integrations with most applications including ticketing or case management, point of sale video integration, telephony systems and more. These API based connectors provide automation and operational efficiency for the security team.

Diagram

Top Use Cases

Know Your Customer (KYC) Violations

fraud detection and prevention by using big data for risk score

Gurucul Fraud Analytics detects account hijacking and fraud abuse in optimal timeframes. It addresses discrepancies and discovers odd behaviors around customer records, such as customer records being updated or changed when they shouldn’t be.

Imagine a bank customer changed their address. The bank distributes a new debit card and sends it to the new address. Then, the address changes back to the original address – only after the issuing of the new debit card. Gurucul Fraud Analytics identities that behavior as anomalous and that customer service representative as risky.

Real-Time Transactional Surveillance

Gurucul Fraud Analytics prevents financial fraud

Gurucul Fraud Analytics uses real-time and near real-time ingestion for transactional surveillance and can identify potential fraudulent transactions on the fly. It discovers suspicious patterns and odd combinations of transactions. Abuse cases include:

  • Merchants submitting false returns and fictitious transactions
  • Merchants performing payment reversals inappropriately
  • Other methods of cyber manipulation of financial transactions and credit card fraud – account takeovers, new account fraud, etc.

Anti-Money Laundering (AML)

AML

Gurucul 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.

An example would be a situation where someone sets up an account, runs a few transactions through that account, and then shuts down the account. When they delete the account, there is no history of the transactions because the account is gone. Gurucul Fraud Analytics has the logs and can discover the anomalous behavior.

Call Center Surveillance

Call Center Surveillance

Gurucul Fraud Analytics tracks call center service representative behavior – shift times, inbound calls, outbound calls, interaction with the phone system and customer systems (i.e., CRM) to ensure customer records are being accessed based on need.

Prevent customer service representatives from accessing data of important customers for personal gain, i.e., “snooping” to obtain PII/PCI data with no business need. Expose representatives who change account properties for self or 3rd party benefit.

The only UEBA solution to be recognized by Gartner for Fraud Analytics in the 2018 Online Fraud Detection Guide.

Fraud Analytics Datasheet
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“The most successful fraud detection and prevention strategies make use of rules and machine-learning techniques.”

– Gartner Market Guide for Online Fraud Detection, 2/5/2018

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