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Fraud Analytics

Monitor Cross-Channel Transactions and Identify Fraud in Real-Time


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)


  • 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 risk analytics models using templates.
  • Real-time Alerting & Fraud Risk Scoring
    Provides real-time predictive fraud 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 on fraud detection.
  • 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 fraud risk analytics 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.


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.



Gurucul provides a holistic risk-based approach for fraud analytics 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.


Fraud Analytics Use Cases

Know Your Customer (KYC) Violations

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 identifies that behavior as anomalous and that customer service representative as risky with fraud risk analytics solution.

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.
Anti-Money Laundering

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 and fraud detection 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.

Call Center Surveillance

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.


How does fraud detection work?

Fraud detection typically involves analyzing data and identifying patterns that may indicate fraudulent activity. There are several methods that can be used for fraud detection, including rule-based systems, anomaly detection, and machine learning algorithms.

  • Rule-based systems involve setting up predefined rules that identify known fraudulent patterns. For example, a bank might set up a rule that flags any transaction over a certain amount as potentially fraudulent.
  • Anomaly detectioninvolves identifying unusual patterns or outliers in data that may indicate fraudulent activity. This can be done by comparing a transaction to historical data or by using statistical methods to identify unusual patterns.
  • Machine learning algorithms can also be used for fraud detection. These algorithms can analyze large amounts of data and learn to identify patterns that are associated with fraudulent activity. The more data that is analyzed, the better the algorithm can become at identifying potential fraud.

Overall, fraud detection involves analyzing data in real-time and identifying patterns that may indicate fraudulent activity. By using a combination of rule-based systems, anomaly detection, and machine learning algorithms, fraud detection systems can become increasingly accurate over time.

What are some tools used for fraud detection in cybersecurity?

There are several tools and techniques that can be used for fraud detection in cybersecurity. These include: Fraud analytics software, User and entity behavior analytics (UEBA), Biometric authentication tools, Digital forensics tools, Intrusion Detection Systems (IDS), and Security Information and Event Management (SIEM) systems.

The tools used for fraud detection in cybersecurity may vary depending on the specific use case, but they generally involve the use of data analysis and machine learning techniques to identify patterns and anomalies that may indicate potential fraudulent activity.

What are some best practices for fraud detection and prevention?

There are several best practices that organizations can follow to improve fraud detection and prevention. These include:

  • Implement strong access controls to ensure that only authorized personnel have access to sensitive data and systems and always use multi-factor authentication.
  • Monitor for suspicious activity using tools like SIEM and UEBA.
  • Train employees on how to identify and report potential fraud.
  • Implement strong fraud detection controls with tools like fraud analytics platforms and data loss prevention solutions.
  • Conduct background checks before granting employees and contractors access to sensitive data and systems to prevent insider threats.
  • Stay up-to-date on emerging threats and stay informed about the latest fraud trends and techniques.

By following these best practices, organizations can establish a robust fraud detection program that can help detect potential fraudulent activities in a timely manner, reducing the risk of financial losses and reputational damage.

What are the 5 most common types of fraud?

  1. Identity Theft: This involves stealing someone’s personal information, such as their name, address, Social Security number, or credit card number, and using it to make fraudulent purchases or open new accounts.
  2. Phishing Scams: This involves sending fake emails, text messages, or websites that look like they are from a legitimate company or organization in order to trick people into giving away their personal information.
  3. Credit Card Fraud: This occurs when someone uses a stolen or fake credit card to make unauthorized purchases or withdraw cash.
  4. Insurance Fraud: This involves making false claims to an insurance company for the purpose of receiving money or benefits. Healthcare Provider Fraud is a $70B a year business; these fraudulent practices are designed to produce additional profits for the provider such as billing for services not provided, prescribing unnecessary drugs, and incorrect/exaggerated diagnosis. Healthcare Consumer Fraud is where individuals commit medical identity theft, falsify claims from non-existent clinics, stage accidents or events or lie about the extent of damages.
  5. Investment Scams: These scams involve promoting fraudulent investment opportunities to unsuspecting investors with the promise of high returns, but in reality, the investments are fake or worthless.

Additional Fraud Analytics Resources