Fraud Analytics

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

Cyber fraud costs organizations billions of dollars each year. Online adversaries are on the rise, as enterprises struggle to analyze ever-growing mountains of data, exceeding human capacity to handle.

Legacy fraud detection platforms have limitations that result in too many false positive alerts, loopholes that allow the fraudulent activity to go undetected, and determinations of fraudulent activity after the fact when it’s too late to prevent the loss. Datasets are limited or siloed, and there is little context around that data to make an accurate assessment of risk. The different channels of potentially relevant data reside in completely different systems and formats. And reliance on rules or policies to make a judgment on the legitimacy of transactions is inherently effective.

Gurucul Fraud Analytics (Business Challenge)
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CRITICAL CAPABILITIES

  • Fraud Prevention and Detection
    Provides user and entity centric (PoS, end point devices, servers, etc.) behavior analytics using 2500+ 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 predictive 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.
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KEY BENEFITS

Detect Financial Fraud
Money laundering, Credit Card fraud, Identity fraud, Mortgage fraud, etc.

Prevent Healthcare & Claims Fraud
Prescription fraud, Claims Fabrication, Upcoding, Unbundling, etc.

Stop E-commerce & Retail Fraud
Charge Back, Unauthorized Discounts, Unauthorized Sale Voiding, Exceptions, Returns, etc.

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WHY GURUCUL?

Gurucul 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.

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Top Use Cases

External, Internal, Cloud Incident Collection and Monitoring

Know Your Customer (KYC) Violations

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 identities that behavior as anomalous and that customer service representative as risky.

Lateral Movement Detection Lateral Movement Detection

Real-Time Transactional Surveillance

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.
Insider Risk and Threat Monitoring

Anti-Money Laundering (AML)

Gurucul 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. Advanced machine learning algorithms build a cross-channel behavior profile and detect any deviation based on real-time activities like abnormal SWIFT transfers, excessive funds transfers, unusual debit card usage and card not present transactions.

Advanced Threat Detection and Response

Call Center Surveillance

Gurucul 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. The flexible data integration framework allows ingestion of data from a wide range of sources including ticketing systems, VoIP phone data, badge access data, workstation events and network events which are linked to the user identity. This allows detection of fraud scenarios including abnormal data transfer and unusual pattern of activities, such as customer profile changes without corresponding ticketing or service request, malicious in-bound or out-bound phone activity, session time, etc.

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 identities that behavior as anomalous and that customer service representative as risky.

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.

Gurucul 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. Advanced machine learning algorithms build a cross-channel behavior profile and detect any deviation based on real-time activities like abnormal SWIFT transfers, excessive funds transfers, unusual debit card usage and card not present transactions.

Gurucul 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. The flexible data integration framework allows ingestion of data from a wide range of sources including ticketing systems, VoIP phone data, badge access data, workstation events and network events which are linked to the user identity. This allows detection of fraud scenarios including abnormal data transfer and unusual pattern of activities, such as customer profile changes without corresponding ticketing or service request, malicious in-bound or out-bound phone activity, session time, etc.

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RESOURCES