How data analytics can transform your internal audit function.
Zennemis – Internal Audit Data Analytics. Internal auditing methods that have been used for decades have worked well. As business environments become more complex, most companies are turning to data analytics methods to identify risks and gain insights. Data analytics is now essential for businesses seeking to understand risks and gather valuable insights in today’s complicated environments.
While management is responsible for reducing risks, internal audit can use data analytics to identify gaps in controls. Internal audit can leverage data analytics to focus on areas where controls are missing or ineffective, assisting management.
What is Data Analytics?
It is very important to know what analytics is: it’s not a tool, it’s an idea. When you use certain technologies, skills, and methods (like data mining tools like IDEA and ACL), you can look into, evaluate, and explore how a business works.
Data analytics is the process of getting useful information from operational, financial, and other types of computer data, whether they are inside or outside of an organization. The ideas can be based on the past, the present, or the future, and they can also be risk-focused.
Why has the use of data analytics increased within internal audit?
IA departments are using data analytics more and more because of a number of reasons. First, the amount of structured (like financial data) and unstructured (like emails and Word papers) data has grown a lot in the past few years. Second, the old-fashioned and hand-written ways of doing internal audits have some problems. For instance, they depend too much on random samples, which means they can’t show you everything about errors, control weaknesses, or risks.
It is crucial to deeply analyze an organization’s data, focusing on the entire population for a comprehensive view. Using groups may overlook risks, so examining the entire data set ensures that all potential risks are identified. Today’s organizations have complex IT and financial systems, making it vital to analyze the complete data population.
Rising stakeholder expectations and the need for internal audit to stay “cutting edge” are key factors to consider. Internal audit must adapt to new methods and keep up with technology to meet stakeholders’ growing demands and needs. One of the biggest challenges for internal audit is becoming more efficient in identifying and managing risks effectively. Another challenge is improving the ability to provide more useful information to stakeholders, enhancing decision-making and risk management. This is where data science can really make a difference.
So where can data analytics transform the internal audit process?
A lot of internal audit teams now use data analytics to look at things like spending, payroll, and accounts payable. There are chances to get your money back in these areas because they are very transactional and policy-driven. On the income side, billing data can be used to make sure that an organization’s bills are in line with contracts and to find mistakes or strange patterns.
There are many ways to sample, which is an important part of any audit work. It’s easy to do statistical sampling when you use statistics tools like IDEA or ACL. This lets the scope be set, which gives useful and defendable information when findings are applied to the whole population.
Here are a few common and easy ways that data analytics can change an internal audit:
Accounts payable
Key risk areas to focus on are often rules over supplier data, like who can see it, change bank information, and authorize payments. By using data analytics, you can find out who has access to supplier data and if there are any division of duties issues. On the other hand, transactional data can be queried to look for fraud, duplicate payments, and other control holes. Here are some of the most important statistics that can be done in this area:
- search for duplicate invoices and payments
- confirm key suppliers, identify one-time suppliers, and suppliers set up with no transactions
- check the bank account details in the supplier master file to employee bank account records, looking for potential fraudulent activity/dummy suppliers
- search for invoices with no corresponding purchase order
- search for unapproved purchase orders
- search for multiple invoices at or just under approval cut-off levels.
Payroll and employee expenses
Data analysis can be very helpful when looking for “ghost employees,” false wage claims, and time sheets that have been tampered with. Data analysis can also be useful because it lets you check that electronic time records are in line with policies, procedures, and job laws. These are expected to be some of the most important analytics:
- search for ghost employees by looking for duplicate National Insurance numbers, addresses or bank account details held on the employee master file
- search of payments made to employees after they have left
- search for unapproved time entry records
- analyse monthly/weekly payroll looking at the hours worked, level of overtime
- search expense claims at or just under approval cut-off levels.
Sales processes
For billing or income stream audits, the IT systems involved can be hard to understand and handle a lot of data, like in a telecoms or utilities company. Data analytics can be a great way to make sure that the bills you send to customers are correct. Any mistakes in billing can be found much more quickly and easily, and they can be measured across the whole community.
When looking through accounts receivable, different types of analytics can be used to find duplicate or missing invoices, records that don’t match up, and bad debts. All of these can show where the credit control process is weak.
Inventory
Due to the size of some stockpiles, data analytics can be used to check the stock. It can be used to find inventory that might be out of date or moving slowly, and it can also give information about the description of the inventory.
Key financial controls
Using data analytics to test financial controls boosts confidence that task separations and access controls are properly checked. Data analytics allows a quick review of general ledger transactions, revealing useful details about journal entries and their approvals.
In short, if internal audit starts using data analytics, they will be able to do a lot of good things. By digging into and trying whole groups of data, it can really change an audit, and it gives useful information about a company’s risks and processes. At this point in time, this is really the only way for internal audit teams to look trustworthy.
Conclusion: Internal Audit Data Analytics
In conclusion, data analytics is transforming internal audit functions by offering more effective methods to identify and manage risks. With organizations increasingly relying on complex IT and financial systems, traditional audit methods have become less effective. By analyzing entire data populations instead of random samples, internal auditors can uncover risks and inefficiencies that would otherwise go unnoticed. Data analytics enhances efficiency, enables a deeper understanding of key areas like accounts payable, payroll, and inventory, and provides actionable insights to improve controls. Furthermore, the growing demands of stakeholders and the need for cutting-edge technology make adopting data analytics essential. Internal audit functions that embrace these tools can enhance their credibility, offer valuable insights, and improve overall risk management. Internal Audit Data Analytics
