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The Association of Certified Fraud Examiners’ (ACFE’s) Report to the Nations: 2020 Global Study on Occupational Fraud and Abuse provides ample evidence that some fraud detection methods are better than others. In general, passive methods, such as accidental discovery or notification by police, coincide with longer-running schemes and higher financial costs. To nab dishonest employees quickly and limit losses, your company needs to be proactive.

Shorten time, minimize costs

Active methods include IT controls, data monitoring and analysis, account reconciliation, management review, surprise audits and internal audit. These methods can significantly lower fraud durations and losses.

For example, frauds detected by IT controls had a median duration of six months and a median loss of $80,000. Those found through account reconciliation ran for a median of seven months and totaled a median loss of $81,000. By comparison, fraud detected through notification by police or stumbled upon by accident had a median duration of 24 months. When companies learned about a scheme from law enforcement, the median loss was $900,000.

Surprise audits and proactive data monitoring and analysis can be especially effective ways to fight fraud. On average, victim organizations without these antifraud controls in place reported more than double the fraud losses, and their frauds lasted more than twice as long as frauds at victim organizations with these controls in place. Yet only 37% of the organizations in the ACFE study had implemented surprise audits or data monitoring and analysis.

Tips are most effective

The leading fraud detection method, tips, could be considered active or passive. But there’s no arguing that this method is effective — particularly when organizations offer employees and other stakeholders confidential fraud hotlines. Organizations that had hotlines for reporting misconduct detected fraud by tips more often (49% of cases) than those without hotlines (31% of cases).

To ensure that tips are used as an active detection method, your organization should set up a hotline and promote its use. Increasingly, companies offer other reporting forms, including email and Web-based submissions. Also, the ACFE has found that in 33% of cases where a tip was made, the whistleblower reported suspicions to a supervisor or other person in a position of authority.

Budget-friendly options

Even if your organization’s budget is tight and you think you have few resources to commit to fraud prevention, know that there’s always something you can do. Active methods can be surprisingly low cost and they certainly are less expensive than being defrauded. Contact us for more information.

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, association analysis 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 analysis, 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 methods

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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Actively look for fraud and reduce financial losses

3 ways fraud experts use data analytics