Fraud analytics

What is fraud analytics?

Organizations face two types of fraud threats, internal (committed by employees, managers, owners, etc.) and external (committed by customers, vendors and other parties). Fraud analytics involves collecting and analyzing large sets of data to detect and prevent fraud. Collected data is mined for patterns and irregularities that are translated into insights used by organizations to identify false transactions, find instances of money laundering, assign fraud risk scores, detect asset misuse, data leakage, privacy issues and more.

Fraud analytics can be used in conjunction with artificial intelligence (AI) to develop effective fraud prevention and fraud detection strategies.

There are a number of different fraud analytic techniques, including:

  • Predictive: By analyzing patterns, predictions on future activity or risk of fraud can be made.
  • Pattern recognition: Identifying activity that strays from the norm can be used to identify fraud.
  • Investigative: The examination of confirmed fraud cases can identify relationships between data and users, helping to detect potentially fraudulent behavior and uncovering cooperation between bad actors.

Benefits of fraud analytics

Users expect brands to provide environments where online transactions can be completed in a safe and secure manner. Additionally, it is imperative to an organization’s bottom line, culture and reputation that instances of illicit activity are addressed. Fraud analytics can be used across various industries to build fraud detection and prevention strategies to make this possible. Other benefits include:

  • Uncovering hidden patterns: Together with AI, fraud analytics can be used to uncover new patterns and trends that traditional efforts were unable to identify.
  • Increased business intelligence (BI): Fraud analytics provide insights from all aspects of a business so that leaders can make informed decisions on how effective a fraud detection strategy is, or what gaps may exist.
  • Increases efficiency: Manually investigating and prosecuting fraud claims can be time-consuming. Machine learning algorithms can analyze hundreds of thousands of transactions per second, with neural networks taking this capability a step further by making decisions in real-time. These technologies are successful in culling the high number of flagged transactions that occur, and providing a concise list of those that require further investigation by a human counterpart.
  • Increases accuracy: Fraud detection technology ensures agents are armed with the proper information and tools to correctly identify fraudulent activity. Augmented intelligence — where machines and humans work together in tandem — streamlines and ensures the accuracy of the fraud detection process.

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