Overview
Audit analytics involves leveraging advanced data analysis techniques in auditing to streamline and optimize the audit process. By applying statistical analysis, data mining, and other analytical methods to vast amounts of financial and operational data, auditors aim to uncover valuable insights, detect patterns, and evaluate risks, enabling them to make more informed decisions. The significance of audit analytics has surged alongside the evolving business landscape and the growing complexity of financial transactions. In essence, audit analytics serves as a potent tool for auditors to navigate the intricacies of modern business environments, boosting audit accuracy, efficiency, and contributing to enhanced risk management, fraud detection, and overall decision-making. With technology's continuous advancement, audit analytics' role is increasingly pivotal in the audit profession.
Why You Should Attend:
1. Enhanced Risk Assessment: Audit analytics allows auditors to conduct a more thorough and comprehensive risk assessment. By analysing large datasets, auditors can identify irregularities, outliers, and potential areas of risk that may not be apparent through traditional audit methods. This enables a more targeted and risk-focused audit approach.
2. Increased Efficiency and Effectiveness: Automation of audit processes through analytics reduces the reliance on manual procedures. Auditors can analyze entire datasets rather than relying on sampling methods, providing a more accurate representation of the financial and operational landscape. This not only improves the quality of the audit but also enhances efficiency by saving time and resources.
3. Timely Detection of Anomalies and Fraud: The use of audit analytics facilitates the early detection of anomalies, inconsistencies, or signs of fraudulent activities. Real-time monitoring and analysis of transactions enable auditors to respond promptly to potential issues, minimizing the impact of fraudulent behaviour on financial statements.
4. Insightful Decision-Making: Audit analytics generates valuable insights into business operations, financial performance, and potential areas for improvement. Auditors can provide more meaningful recommendations to clients, helping them make informed decisions based on data-driven insights.
5. Adaptation to Technological Advances: With the increasing reliance on technology in business operations, audit analytics enables auditors to keep pace with technological advances. It allows for the examination of electronic records, digital transactions, and other technology-driven aspects of financial reporting.
6. Regulatory Compliance: In a rapidly changing regulatory environment, audit analytics aids in ensuring compliance with industry regulations and accounting standards. By incorporating analytics into the audit process, organizations can demonstrate a commitment to transparency and accountability.
Who Should Attend:
1. External Auditors and Internal Auditors
2. Financial Analysts and Accountants
3. Risk and Governance Professionals
4. Compliance Professionals
5. IT and Data Professionals
6. IT Audit Professionals
7. Finance Professionals
8. Internal Control Professionals
9. Assurance Professionals
Training Objectives:
Stay ahead in the ever-evolving field of auditing by joining our hands-on Audit Analytics Training Course. Gain practical experience with real-life examples and case studies, allowing you to seamlessly integrate advanced data analysis techniques into your daily audit procedures. Learn to identify potential risks and anomalies more effectively, enhancing your risk assessment capabilities for a more strategic approach to auditing.
Improve efficiency through automation, analyze entire datasets, and reduce reliance on manual processes. Engage in interactive sessions, collaborate with professionals, and enhance your understanding of audit analytics through discussions and hands-on exercises. Future-proof your auditing expertise by acquiring skills aligned with the latest technological advancements.
Learning objectives:
Understand the types of datasets and data analytics
Understand the application of Data Analytics in Audit
Apply Descriptive Analytics in Audit
Perform Diagnostics Analytics
Build Predictive Models using Linear and Linear Regression Models
Training Outline Modules:
Day 1
Definition
• Types of data analytics
• Application of data analytics in audit
• 5-step framework
Understanding of data
• Data types
• Databases
• Normalised vs denormalised databases
Introduction to Descriptive Analytics with Excel
• Understanding of basic statistics
• Exercises 1 and 2: Data Analytics with Pivot and Pivot Charts
• Exercises 3 - 14: Data Visualisation with Excel
Audit Case Studies
Case Study 1: Profit margin analysis with disaggregated data with Box and Whisker Plot. Evaluation of statistical and non- statistical outliers
Case Study 2: 100% re-computation of revenue, followed by data visualisation.
Case Study 3: Inventory Valuation Analytics
Day 2
Introduction to Diagnostic Analytics with Excel
• Understanding of intermediate statistics
• Exercise 15: Hypothesis Testing
• Exercise 16: Correlation analysis
Audit Case Study
Case Study 4: Payroll Analytics
Introduction to Predictive Analytics with Excel
• Understanding of regression modelling
• Exercise 17: Linear Regression Model
• Exercise 18: Multiple Linear Regression Model
Audit Case Study
Case Study 5: Predicting revenue with drivers.
Introduction to Prescriptive Analytics with Excel
Introduction to Goalseek and Solver functions in Excel
Audit Case Study:
Case Study 6: Optimisation of Production Plan with Solver
Trainer's Profile:
Chong Yu brings over 16 years of diverse experience in financial audit and data analytics across various industries, including stock exchange, investment funds, banking, retail, manufacturing, and more. He currently shares his wealth of knowledge as a lecturer for the accounting analytics elective at Nanyang Technological University (NTU), and previously served as an Adjunct Lecturer at Singapore Management University (SMU), teaching Audit Analytics. Chong Yu is a seasoned trainer on data analytics, collaborating with professional bodies like the Institute of Singapore Chartered Accountants and Deloitte Learning Solutions. He co-authored a chapter on "Data Analytics in Audit" in a publication by CPA Australia and SMU School of Accountancy. As a former Director at Deloitte, he led a regional Southeast Asia data analytics team, delivering innovative solutions to audit and non-audit clients. Chong Yu holds a First Class Honours degree in Accounting from the University of Hertfordshire, UK, and is a Chartered Accountant of Singapore, an ASEAN Chartered Professional Accountant, and a Fellow Member of the Association of Chartered Certified Accountants (FCCA).
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