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The digitalisation of processes within the higher education sector leads to increased data generation. This data can be an essential asset when leveraged correctly.

Internal Audit also has a role to play in extracting value in these data assets by implementing audit processes underpinned by data analysis and the generation of actionable insights. Several areas within the higher education sector provide opportunities for Internal Audit departments to generate and leverage these insights in addressing risks

A key process within institutions of higher learning is the revenue process. Several risks are related to this process, which include:

  • Incorrect billing of students for subjects taken.
  • Incorrect application of fee structures for subjects. 
  • Incorrect billing for accommodation used.
  • Incorrect accounting and allocation of student revenue.
  • Credit losses due to poor monitoring of student debt.

Continuous auditing provides a practical approach to identifying, quantifying, and managing these risks. Using automated data analytics, Internal Audit departments can get early warnings, enabling them to play an advisory role in the line function. Some examples of these analytics include:

  • Exception reports - Performing periodic comparisons of fees charged against subjects taken and accommodation used and producing exception reports for revenue leakage. Furthermore, automated reconciliations can be performed between student billing systems and the accounting system to determine discrepancies.
  • Data visualisation – Using data visualisations to unearth discrepancies in the application of fee structures. 
  • Predictive modelling – Using artificial intelligence techniques to predict debtor accounts that may go bad, enabling the Institution to take timeous corrective measures

 

In addition to revenue collected from students, grant funding makes up a significant portion of institutions' income. A few risks are related to this, including:

  • The use of earmarked funds on projects that the funds were not intended for.
  • Potential embezzlement of the funds.

In response, Internal Audit departments can leverage data analytics to perform continuous matching of uses and sources of funds to determine if funds are used in an intended manner. In addition, data analytics can be used to constantly monitor who the recipients of the funds are and highlight any suspicious transactions for further analysis.

The supply chain management process plays a critical role in ensuring that an organisation derives value in the goods and services it purchases. However, far too often, the deficiency in this process not only leads to a loss of value but can also be exploited for fraudulent purposes. Automated and continuous data analytics can be used in this process, including:

  • Automated process analytics – These analytics can be performed on supply chain data that include requisitions, purchase orders, goods receipts, invoices, and payments. Exceptions can be produced to show where the process is not working as intended or is being circumvented. On top of the analytics, data visualisations and statistical analyses can discover patterns that help unpack the root causes of the process deficiencies.
  • Expenditure analytics – Data analytics can be continuously performed on contract and payment data, highlighting instances where spending is starting to exceed contract limits, and empowering line functions to avoid excessive spending above what is permitted.

One of the areas that can easily benefit from Internal Audit performing continuous monitoring is human resources and payroll.

Given the nature of the employee base at most institutions of higher learning, where there is a large component of contracted or non-fulltime employees, there are additional people-related risks that should be monitored. Automated data analytics can be used to:

  • Assess the completeness and validity of employee information.
  • Identify potential ghost employees.
  • Identify potential conflicts of interest between employees and vendors.
  • Identify excessive changes in pay that warrant further scrutiny.

With the advent of learning from home, education has leapt into the world of technology. Hand in hand with this comes the digital administration, curation, marking and moderation of examinations, and collation of student marks.

These bring along new risks that did not exist in a world of paper-based examinations. In this respect, data analytics can be used to identify anomalies in the examination process that can include:

  • Accuracy in marking (for certain types of examinations).
  • The consistency in marked results and moderated results.
  • The recording of results.
  • The use of artificial intelligence models to detect anomalies in examination results that are difficult to identify using traditional analytics methods.

Student Representative Council ("SRC") elections can be a major operation for an institution of higher learning. These elections can also be highly contested, requiring the process to be robust and not open to manipulation.

Internal Audit can play an important oversight role in these elections. Data analytics can also be used to give Internal Audit comfort on the validity of the process and the results. Some of the data analytics that can be performed in this respect include:

  • The verification of student registrations against the voters' roll to ensure all and only registered students can vote.
  • Comparison of votes submitted and votes received to identify any discrepancies.
  • Comparison of votes received against the voters' roll to determine any discrepancies in the voting process.