Lecture 4 Risks of Material Misstatements (2)

Assessing the Risk of Material Misstatement

Chapter Overview

  • Focus on understanding risks in auditing, specifically the risk of material misstatement.

Chapter 9 Learning Objectives

  1. Define Risk in Auditing

  2. Distinguish Different Types of Risk Assessment Procedures

  3. Understand Auditor Considerations Related to Fraud

  4. Describe Auditor’s Responsibility for Significant Risks

  5. Explain the Audit Risk Model and its Components

  6. Assess Acceptable Audit Risk

  7. Consider Factors Affecting Inherent Risk Assessment

  8. Discuss the Relationship Between Risk and Audit Evidence

  9. Explore the Relation of Materiality to Risk in Auditing

  10. Utilize Smart Analytics in the Audit Process

Risk in Auditing

Definition of Risk

  • Auditors accept a certain level of risk when performing audits.

  • Material Misstatement Risks:

    • Overall Financial Statement Level: General risks that affect multiple transactions.

    • Assertion Level: Divided into two components:

      • Inherent Risk: Likelihood of a material misstatement occurring.

      • Control Risk: Risk of internal controls failing to prevent or detect misstatements.

Risk Assessment Procedures

  • Types of Risk Assessment Procedures:

    • Inquiries with management and stakeholders.

    • Analytical procedures.

    • Observation and inspection.

    • Team discussions.

    • Other relevant risk assessment methods.

Fraud Risk Considerations

  • Assess material misstatement risk due to fraud at:

    • Financial Statement Level

    • Assertion Level for transactions and disclosures.

  • High-profile fraud cases necessitate a presumption of fraud risk in revenue recognition.

Auditor Responsibility for Significant Risks

Identification of Significant Risks

  • Significant risks need special audit consideration based on:

    • Probability of occurrence and potential impact.

  • Non-routine Transactions:

    • Related-party transactions typically signify greater risk.

    • Estimates involving significant measurement uncertainty also warrant attention.

    • Fraud risks are generally treated as significant risks.

Audit Risk Model

Components of the Audit Risk Model

  • Helps auditors determine evidence requirements for audit objectives.

  • Involves assessing:

    • Planned Detection Risk: Risk of not detecting misstatements.

    • Inherent Risk: Susceptibility of assertions to material misstatement.

    • Control Risk: Risk of undetected misstatements due to ineffective controls.

    • Acceptable Audit Risk: Auditor's willingness to accept potential misstatements post-audit.

Assessing Acceptable Audit Risk

  • Engagement Risk: Audit firm’s exposure to harm regardless of audit accuracy.

Factors Affecting Acceptable Audit Risk

  • External Users’ Dependency: Client size, ownership distribution, liabilities.

  • Client Financial Stability: Liquidity, past performance, growth financing methods.

  • Management Integrity Evaluation.

Assessing Inherent Risk

Factors for Inherent Risk Assessment

  • Nature of the business, prior audit outcomes, engagement types (initial vs. repeat).

  • Consider:

    • Related parties,

    • Complexity of transactions,

    • Judgments for accounting accuracy,

    • Potential fraudulent reporting and asset misuse.

Relationship of Risk to Audit Evidence

  • As risk levels change, so do evidence requirements:

    • Vary according to acceptable audit, inherent, control, and planned detection risks.

  • Responses to risks may involve:

    • Utilizing experienced staff,

    • More thorough engagement reviews.

Relationship of Risk and Materiality to Audit Process

  • Materiality: Magnitude implications; intertwined with risk assessments.

  • Understanding how performance materiality interacts with risk factors is crucial for audit evidence planning.

Smart Analytics in the Audit Process

  • Leverage analytics for identifying risk attributes and anomalies.

  • Benefits include:

    • Customized solutions,

    • Enhanced data accuracy.

  • Cautions against:

    • Flawed modeling, assumptions, coding errors that can elevate operational risks.