Forensic accountants have long used technological tools to uncover fraud schemes. But recent advances in “big data” have provided even better, more efficient techniques for identifying suspicious activities and dishonest employees. These are three common types of data analytics used by fraud experts:
1. Association analysis
This method can help identify suspicious relationships by quantifying the odds of a combination of data points occurring together. In other words, it calculates the likelihood that if one data point occurs, another will, too.
If data point combination occurs at an atypical rate, a red flag goes up. For example, these analytics might find that a certain worker or manager tends to be on duty when inventory theft occurs.
2. Outlier analysis
Outliers are data points outside the norm for a given data set. In many types of data analytics, outliers are simply disregarded, but these items come in handy for fraud detection. Experts know how to distinguish and respond to different types of outliers.
Contextual outliers are significant in certain contexts but not others. For example, a big jump in wages on a retailer’s financial statements might be notable in April but not in December, when seasonal workers usually come aboard.
Collective outliers are a collection of data points that aren’t outliers on their own but deviate significantly from the overall data set when considered as a whole. If, for instance, several public company executives sold off substantial blocks of stock in the business on the same day, it might indicate suspicious behavior.
3. Cluster analysis
Here, experts group similar data points into a set and then further subdivide them into smaller, more homogeneous clusters. Data points within a cluster are similar to each other and dissimilar to those in other clusters. The greater the similarities within a cluster and the differences between clusters, the easier it is for an expert to develop rules that apply to one cluster but not the others.
Cluster analysis has long been used for market segmentation of consumers. But it can also detect fraud, particularly when combined with outlier analysis. Outlier clusters — those that are farthest from the nearest cluster when clusters are mapped out on a chart — generally merit extra scrutiny for suspicious activity.
Fraud experts might, for example, use cluster analysis to evaluate group life insurance claims. They then would look for clusters of large beneficiary or interest payments, or long lags between submission and payment.
Old school analytics
Of course, technology alone usually doesn’t make the case against an employee. Face-to-face interviews and other “old school” methods are crucial to identifying fraud perpetrators and learning where they’ve stashed the money they’ve stolen. If you suspect fraud in your organization, contact us to investigate.
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