Case Study: How data analytics helped uncover hidden risk in a client’s business

Are you curious about the benefits of your financial statement audits? A financial statement audit offers more benefits, including ensuring that management has given a "true and fair" view of the company’s financial performance and position. During an audit of your financial data, you should expect assurance, compliance, insights, and recommendations. 

This case study explores how BDO Ghana used data analytics to uncover hidden risks in a client’s business, going beyond the traditional audit approach.

Background

In today’s business environment in a high-volume transaction setting, the traditional audit approach is inefficient, more costly, and vulnerable to human mistakes. 

Our client, Company A, operating in the agricultural sector, recently appointed us as their auditors, citing instances of the difficulties they encountered with conventional auditing techniques, which were periodic and reactive, as it frequently overlooked important problems because they relied on dispersed sampling. 

Objectives of our involvement

Our objective was to identify hidden risks and provide deeper insights into the company’s financial health. 

What we brought on board

Following our commitment to continuous improvement and adaptation of technology supported by the International Standards on Auditing (ISA), we re-engineered our audit process, incorporating statistical methods, Artificial Intelligence (AI), Machine Learning (ML) and data analytics techniques. 

The new audit methodology, Audit Data Analytics (ADA), which integrates analytics into the auditing process, facilitates the following goals for our client's business.:

  • Evaluate the entire population and perform an in-depth analysis of financial records, encompassing expenses, assets, liabilities, income, and other related accounts.
  • Uncover hidden risks that may impact business operations and mitigate potential threats such as accounting errors and fraud.
  • Highlight risk areas within financial records leading to audit effectiveness and efficiency.
  • Reveal meaningful insights and offer suggestions to improve financial performance.

What did we do?

Data Collection and Preparation

In addressing this, we gathered comprehensive financial records from Company A, encompassing expenses, assets, liabilities, and revenue. Our data collection and preparation process included:

  • Gathering data, including historical data up to 4 years from their accounting systems.
  • Cleaning, transforming, and manipulating the data to ensure accuracy and consistency.
  • Addressing missing data and normalizing rows and columns to ensure data integrity.

Analytical Methods and Tools Used

 

We employed advanced analytical tools and techniques, including:

  • Audit Data Analytics (ADA): Analyzed the entire population of financial transactions.
  • Data Visualization: Visualized financial metrics and trends.
  • AI & ML Modeling: Identified potential risks and anomalies in the financial data.

Key Findings
 

Our analysis uncovered multiple significant risks for the year under review:

  • Expenses Surge: Company A's expenses increased from $380,000 to $1.3M.
  • Year-End Spending Spike: A significant amount of expenses at the year-end.
  • Recurring Refunds: Frequent refunds on vehicle expenses every three months.
  • Duplicate Invoices & Payments: Detection of duplicated invoices and payments.
  • Missing Invoices: Identified gaps in invoice numbers, risking incomplete revenue reporting.

Actionable Insights and Recommendations
 

Based on the findings, we provided the following actionable recommendations:

  • Expense Management: Cost control strategies to reduce skyrocketing expenses.
  • Revenue Integrity: Ensure all revenues are accounted for to address invoice gaps.
  • Internal Controls: Create a document numbering system that reduces duplicates and gaps.
  • Operational Efficiency: outline audit points to reduce risk and improve efficiency.

Impact and Results
 

The implementation of our recommendations subsequently led to:

  • Improved Financial Performance: Significant reduction in unnecessary expenses.
  • Enhanced Data Quality: Better data management practices and financial reporting.
  • Efficient Audits: Reduced audit turnaround time. We received positive feedback, with Company A expressing confidence in their improved financial health and the audit process.
Lessons Learned
 

Through this audit, we learned the importance of:

  • Data Quality: Ensuring accurate and complete data to prevent financial discrepancies.
  • Communication: Communicating effectively with clients is essential to understand their needs, serve them better, and address issues promptly.

Conclusion

This case study demonstrates the power of data analytics in uncovering hidden risks and enhancing the audit process. By embracing data-driven techniques, accountants can provide deeper insights and more reliable outcomes. Investing in data analytics will become a benchmark for delivering exceptional value to clients as the accounting sector evolves.