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Detecting financial fraud using the Splunk App for Behavioral Profiling

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Detecting anomalous behavior in financial transactions is not just a security feature; it is a business necessity. Financial institutions handle millions of transactions daily, making it increasingly challenging to distinguish between normal operations and potential threats. The Splunk App for Behavioral Profiling tackles this challenge by offering an advanced yet practical approach to monitoring and analyzing user behavior across financial systems.

The Splunk Behavioral Profiling App referenced in this article is not officially supported by Splunk and is discussed only to demonstrate the "art of the possible" in security analytics. While not recommended for production environments, security professionals are encouraged to explore these concepts in lab or test environments to gain insights into behavioral profiling approaches. For production solutions, please contact Splunk Professional Services.

Data required

Financial systems data, for example from trading platforms, authentication systems, and transaction processors

Solution

The Splunk App for Behavioral Profiling utilizes a three-tier architecture to process and analyze data in real time. The system ingests data from various financial sources—including trading platforms, authentication systems, and transaction processors—through the Splunk standard data collection framework.

The app's machine learning engine analyzes historical patterns to create baseline profiles for entities (users, accounts, or systems). These baselines continuously evolve to reflect legitimate changes in behavior while flagging potentially suspicious activities. This dynamic approach helps reduce false positives, which are a common challenge in traditional rule-based systems.

To try the app now, check out the click-through demo.

Use cases for the app include:

  • Trading behavior analysis: The system observes trading patterns across various asset classes, identifying unusual trading volumes, changes in frequency, or unexpected trading opportunities. For instance, when a trader significantly deviates from their typical trading volume, the system flags this for review while considering market conditions and historical patterns.
  • Fraud detection: The app can identify potential fraud scenarios in real-time by analyzing user sessions, transaction patterns, and access behaviors. A typical example is detecting when an account holder's behavior suddenly changes across multiple channels, such as unusual login times and atypical transaction patterns.
  • Compliance monitoring: The app helps institutions meet regulatory requirements by offering comprehensive audit trails and behavioral analytics that align with frameworks like MiFID II and MAR. It automatically logs behavioral anomalies that might suggest market manipulation or insider trading.

Next steps

Based on real-world implementations, the best practices outlined here offer a framework for successful deployment and ongoing maintenance:

  • Profiling and anomaly scoring: Security teams should leverage at least 30 days of historical transaction data to establish meaningful baselines and thresholds. A data-driven approach to initial configuration helps teams avoid the common pitfall of alert fatigue while ensuring anomaly detection remains contextually relevant.
  • Business hours: Systems and users typically behave differently during core business hours compared to off-hours or weekends. Distinct profiles for these varying periods can significantly enhance detection accuracy.
  • Detection and alert tuning: Starting conservatively helps prevent alert fatigue. Setting initial thresholds slightly above calculated baseline deviations allows for adjustments as the system evolves. Establishing distinct thresholds for different organizational groups is crucial.
  • Performance management: Every environment has its optimal balance between comprehensive coverage and system resources. Starting with critical assets and expanding organically proves more effective in practice. Monitor active profile counts and update intervals closely.
  • Maintenance and optimization: Effective behavioral profiling requires regular maintenance. Monthly reviews should involve archiving inactive profiles, validating data source mappings, and adjusting thresholds based on falsely favorable rates.

In addition, these resources might help you understand and implement this guidance: