Human Resources

Application: Audits for Human Resource Applications

By: Robert Denker, CISA, CFE, CIA, Managing Consultant – Special Investigation Unit, MetLife Insurance Company

While there appears to be no shortage of applications that are candidates for conventional auditing tools such as Caats, there have not been many such examples for the ‘exotic tools’ such as data mining and data visualization software. This situation should change dramatically as auditing units start to achieve some success using these tools. Human Resource Applications represent one of the virgin areas for data mining.

First, a definition of what exactly are Data Mining tools. While there is little consensus as to the definition of data mining software, let us for argument sake say that it models the database for the purpose of determining patterns and relationships among the data. The major difference among the data mining tools available today is how they go about their job. There are high-end data mining products which uses neural networks, can deal with terabytes of data, but can cost upwards of $100,000 and there are the low-end data mining tools; such as WizSoft’s WizRule® which utilizes special sets of algorithms to generate reports of if-then rules and formulas and which cost a little over $1,000.

  • Employees being paid salaries above the range for a specific level.WizRule will indicate anomalies based upon deviations from salary averages for specific levels. While these may or not point to problem area, auditors should satisfy any concerns that may have related to the deviations.
  • Employees who live far from their normal office of employment.If you include the distance from the home to the office or time to travel this distance as a variable, WizRule will determine any variances from the norm. While on the surface this may appear to be an unimportant finding, it can point out some very serious areas that should be of concern to the auditor. Employees who have to travel great distances to get to work are good candidates for excessive lateness, absences and eventually leaving for a position closer to their residence.
  • Employees who do not avail themselves of most company benefits.Employees, who do not participate in 401K matching programs, medical benefit programs, etc., may be trying to hide something. While this is not always the case, it is area worth investigating. (E.g. the employee is receiving two payroll checks!).
  • Employees who are the only ‘type’ of a specific employee in a location.As an example, WizRule will indicate that 67 of 68 employees in the Detroit office belong to the Central Claims group. One employee belongs to the Southern Claims group. (Again a possible ghost employee).
  • Employees who work more overtime than the representative population.WizRule will calculate the mean of specific groups and report any anomalies.

These are just a few examples that can be employed using Data Mining Tools in most Corporate Human Resource Systems.