4.0. Application of data analytics in specialised areas

4.2. Auditing

Test controls (specifically segregation of duties) by identifying combinations of users involved in processing transactions

What are test controls?

Test controls, also known as control testing or control tests, are procedures and activities performed by auditors or internal control assessors to evaluate the effectiveness of an organization's internal controls. Internal controls are policies, procedures, and mechanisms put in place by an organization to manage risks, ensure compliance with regulations, and achieve its operational and financial objectives. Test controls are essential to assess whether these internal controls are functioning as intended and are capable of preventing or detecting errors, fraud, or other issues.

Identify segregation of duties concerns by pinpointing user combinations involved in transaction processing within test controls.

Segregation of Duties (SoD) concerns are critical in business data analytics and transaction processing to maintain a strong internal control environment. Here, we'll identify these concerns by pinpointing user combinations involved in transaction processing within test controls, highlighting their importance and offering insights on how they relate to business data analytics:

User Roles and Responsibilities:

Businesses often have multiple users involved in transaction processing, including data entry, approval, and review.
SoD concerns arise when a single user is responsible for multiple critical tasks within the same transaction, such as data entry and approval. This lack of separation can lead to fraud or errors.

Financial Transactions:

In financial analytics, SoD concerns are paramount. For example, a user who initiates payments (e.g., accounts payable) should not also be responsible for approving those payments. This ensures a checks-and-balances system within financial processes.

Access Control and Permissions:

Business data analytics relies on access control to sensitive data. SoD concerns emerge when the same user has access to both data preparation and data analysis tools.
Separating access between data preparation and analysis ensures data integrity and prevents biased reporting.

Data Cleansing and Reporting:

In data analytics, data cleansing and reporting are key steps. A SoD concern may arise if the same user is responsible for cleaning data and generating reports.
Separating these roles ensures that reporting remains unbiased and accurate.

Inventory Management:

In the context of inventory management, a SoD concern occurs when a single user can both approve inventory orders and update inventory records.
Separating these roles prevents discrepancies in inventory levels and potential theft.

Exception Handling:

In data analytics, exceptions and anomalies are common. SoD concerns arise when the user responsible for detecting exceptions is also responsible for approving transactions related to those exceptions.
Separating these roles maintains objectivity and prevents fraud.

Compliance and Regulations:

Many industries have specific compliance requirements (e.g., Sarbanes-Oxley Act). SoD is crucial in such cases to ensure compliance.
Violating SoD can lead to regulatory fines and reputational damage.

Data Privacy and Security:

SoD extends to data privacy and security in analytics. A user handling sensitive customer data should not also be responsible for data encryption and access control.
Maintaining SoD safeguards customer information and prevents data breaches.

Audit Trails and Monitoring:

In the realm of business data analytics, audit trails and monitoring are vital. Users responsible for auditing and monitoring data should not have the ability to manipulate that data.
This prevents cover-ups or malicious actions.

Automation and AI:

As automation and AI become more prevalent in analytics, SoD concerns may emerge regarding who controls and monitors these systems.
Proper SoD is necessary to ensure that automated processes are used ethically and accurately.

Segregation of Duties concerns in transaction processing and business data analytics are vital for maintaining transparency, accuracy, security, and compliance. Recognizing and addressing these concerns is essential to minimize risks, promote data integrity, and enhance the overall effectiveness of data-driven decision-making processes within organizations.