Next-level data analytics for fraud prevention and detection
WHITE PAPER |
Today’s organizations generate and manage tremendous amounts of data, from transaction logs and demographic information to social media activity and online traffic data. In addition to this information, companies with strong business intelligence capabilities extend beyond internally collected data, utilizing third-party databases to gain deeper analysis and insights.
The increasing number of affordable and powerful tools allows companies to develop extensive analytics platforms that extend beyond sales and into other key areas of the business. In fact, data analytics should be a critical weapon in every organization’s fraud protection strategy to guard against loss and reduce vulnerability to regulatory enforcement.
Statistical sampling is typically utilized during internal audit procedures or in response to a suspicion of fraud, but that inherently only focuses on a portion of the data, and leaves vast amounts of unanalyzed information. Conversely, data analytics utilizes forensic techniques to analyze the entirety of the population of data, looking for unusual characteristics that might indicate fraud, enabling high-risk transactions to be flagged for further examination.
By leveraging data analytics, companies can identify potential fraud, waste and abuse and develop proactive steps to minimize the risk of future misconduct. Read our white paper to understand more about the capabilities of data analytics, and three techniques that organizations can use to bolster anti-fraud programs:
- Geospatial analytics: In recent years, organizations have used geotagged and georeferenced information to gain insights into operational performance and customer behavior patterns, but this data can also detect anomalies that may indicate a heightened risk for misconduct.
- Affinity analysis and association rule learning: These tactics have been staples of retail strategies for decades, but they are also effective in an anti-fraud context to identify typical business patterns and flag outliers for investigation.
- Online data extraction: Data mining methods can ease the burden of collecting data when investigating potential asset misappropriation or corruption, compiling it into a structured format, capable of cross-referencing and further analysis.
With fraud becoming more complex and increasingly difficult to detect, companies should look to leverage next-level fraud analytics to strengthen efforts to prevent, detect and mitigate illicit activity.