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Analyzing fraud

Some organizations are quick to pursue a big data approach to combating fraud. Conventional wisdom says big data presents a “plug-and-play” solution to the vexing problem of fraud. But, before thinking about how Big Data and sophisticated tools can help fight fraud, waste, and abuse, organizations should first perform a fraud risk assessment. Such an assessment will help agencies determine what risks are best mitigated by an analytics-oriented approach, which can often improve anti-fraud efforts.

After conducting a thorough risk assessment, agencies may find “small data” efforts utilizing advanced data analytic techniques would be just as effective and better aligned to the organization’s capacity to adopt new approaches. In that case, a big investment in big data might have been a costly mistake.

Building upon a fraud risk assessment, a data analytics assessment can examine the organization against a set of target capabilities, leading to a data analytics strategy of how it will support the risk management operation from a strategic perspective. The data analytics strategy can consider different types of fraud detection – basic fraud detection through pay and chase, rules-based detection, and a combination of rules and advanced data analytics to prevent fraud before it occurs.

Read more about analyzing fraud and how to protect your organization.

This article was originally published in the April edition of Thinktank magazine.