Enterprise Fraud Monitoring
Armed with machine data and machine learning, fraud teams can search, detect and investigate data to quickly find anomalies—reduce loss of money, reputation and organizational inefficiencies.
Learn how to solve security challenges with Splunk
Splunk delivers integrated enterprise fraud management software that quickly defines behavior patterns and protects enterprise information
Proactive Rules and Monitoring
Search and monitor any data with custom rules and automation
Detect and investigate outliers via anomalies in machine data that will help pinpoint fraudulent activity
Why Splunk for Enterprise Fraud Monitoring?
Splunk helps organizations search, detect, investigate and visualize fraudulent behaviors and transactions to determine the anomalies that typically slip through undetected. Take the appropriate steps to detect compromised user accounts.
Splunk defines fraud rules on wire transfer, card transactions to identify suspect activity. It also makes it possible to implement multiple velocity-based rules, such as geographic and merchant changes, and more to determine indications of fraudlent transactions. Splunk can also better identify anomalous behavior utilizing the Machine Learning Toolkit (MLTK). The Clustering algorithm considers multiple fields in the transactions to identify outliers.
Splunk helps healthcare providers identify anomalous providers with highly abnormal prescription drug distributions and volumes compared to peers.
Splunk also helps organizations with billing to identify anomalous providers with highly abnormal current procedural terminology (CPT) code submissions and volumes compared to peers—get better visibility into each provider and their specialty. Healthcare program administrators of third-party consultants use Splunk to employ techniques that allow them to index, analyze, interpret and transform program, case management, and EMR data to help detect potential instances of fraud and implement fraud monitoring programs.
Spunk offers insights to identify unusual trends, data anomalies and control breakdowns, by developing repeatable tests and, in some cases, even serve as an early warning systems before fraud becomes material.