The Digital World Flashcards
How was Auditing conducted in the pre-computer era
Paper-based systems: Auditors worked with handwritten ledgers (nominal ledgers, day books), invoices, purchase orders, and delivery notes.
Manual verification: Every transaction was traced by hand, with auditors checking calculations (e.g., totals, balances) and reconciling entries across multiple books.
Time-intensive: Audits required physical access to documents, making the process slower and more prone to human error than digital methods.
Key Challenge: Lack of automation meant higher reliance on auditor expertise to detect discrepancies.
What was Sage, and how did it transform accounting/ auditing
First mass-market accounting software: Launched in the 1980s, Sage automated tasks like payroll, bookkeeping, and financial reporting.
Significance:
Replaced manual ledger systems for small/large businesses.
Paved the way for modern cloud tools (Xero, QuickBooks) by proving software could handle core accounting functions.
Auditing impact: Reduced manual errors and provided standardized digital records, making audits more efficient.
Limitation: Early versions were desktop-based, lacking real-time collaboration (unlike later cloud systems).
What is Cloud Computing, and how does it work
On-demand delivery of computing services (storage, software, processing) over the internet.
Key Features:
Remote hosting: Data/apps are stored on third-party servers (e.g., AWS, Azure).
Scalability: Users pay for only what they use, avoiding upfront hardware costs.
Accessibility: Data can be accessed anywhere with an internet connection.
Examples: SaaS (Software-as-a-Service) like Xero, IaaS (Infrastructure-as-a-Service) like AWS.
What does ‘auditing the cloud’ entail, and how is it different
Focus Areas:
Data security: Assessing encryption, access controls, and compliance (e.g., GDPR).
Third-party risks: Evaluating cloud providers’ reliability and backup protocols.
System integrity: Ensuring cloud-based financial records are accurate and tamper-proof.
Challenges vs. Traditional Audits:
Less physical control over data (relies on provider’s infrastructure).
Requires understanding of shared responsibility models (client vs. provider controls).
Auditor’s Role: Must verify both the client’s use of the cloud and the provider’s safeguards.
How does Cloud Technology Improve auditing processes
Efficiency Gains:
Real-time data - Auditors access live records without waiting for physical documents.
Automation - Tools auto-flag discrepancies (e.g., duplicate invoices).
Collaboration - Multiple auditors can work simultaneously on shared files.
Risk Reduction:
Version control - Cloud logs track changes, reducing tampering risks.
Disaster recovery - Data backups minimize loss compared to paper systems.
Limitations: New risks like cybersecurity threats or vendor lock-in must be managed.
Example: Cloud-based audit trails in QuickBooks simplify transaction tracking.
What are the 5 Primary Concerns when Auditing Cloud-Based Systems
Loss of Control - Entrusting data to third-party providers reduces direct oversight of infrastructure and processes.
Security Risks - Vulnerabilities to hacking, data breaches, or unauthorized access due to shared infrastructure.
Data Integrity - Ensuring accuracy and consistency of data managed externally (e.g., no unauthorized alterations).
Privacy Compliance - Adherence to regulations (e.g., GDPR) when sensitive data is stored/processed remotely.
Availability - Reliance on provider uptime; outages could disrupt audit timelines.
Why It Matters: Auditors must assess provider safeguards and contractual agreements to mitigate these risks.
Who owns Data stored in the Cloud
General Rule: The entity (user/organization) uploading data retains ownership.
Provider Rights: Cloud vendors may claim limited rights to process/store data (e.g., for maintenance).
Key Consideration: Ownership terms are defined in the Service-Level Agreement (SLA).
Audit Implication: Auditors must review SLAs to confirm ownership clauses and usage rights.
Who controls Cloud-Hosted Data
Shared Responsibility Model:
User Controls - Access permissions, data entry, and user management.
Provider Controls - Physical infrastructure, network security, and backup protocols.
Critical Factor: Control boundaries vary by provider (e.g., AWS vs. Azure).
Auditor’s Task: Verify if the client’s control measures align with their compliance needs.
Is Cloud Data safe from Hacking or Theft
Provider Protections: Encryption, firewalls, and intrusion detection systems are standard.
Persistent Risks:
Shared infrastructure exposes “side-channel” attack risks.
User negligence (e.g., weak passwords) compromises security.
Auditor’s Role: Evaluate both provider and client security practices (e.g., multi-factor authentication).
Reality: No system is 100% secure, but risks can be mitigated.
Is Cloud Data always available when needed
SLAs Guarantee Uptime (e.g., 99.9%), but outages occur due to:
Cyberattacks (e.g., DDoS).
Provider technical failures.
Audit Impact:
Delays if data is inaccessible during critical periods.
Need for backup access plans (e.g., offline copies).
How does the ACCA define Big Data in Auditing
Extremely large collections of data (data sets) that may be analysed to reveal patterns, trends and associations, especially relating to human behaviour and interactions.
Key Points:
Focuses on volume and analytical purpose
Example: Analysing millions of crypto transactions to detect fraud patterns
Audit Relevance: Enables population-wide analysis vs. traditional sampling
What are the Five Key Characteristics (Vs) of Big Data
Volume: Massive scale (e.g., Ethereum’s entire transaction history)
Variety: Structured (trade logs) & unstructured data (customer emails, news)
Velocity: Real-time generation (e.g., live crypto price fluctuations)
Veracity: Data accuracy challenges (e.g., verifying blockchain extraction tools)
Value: Insights for decision-making (e.g., fraud detection in full transaction sets)
How do Volume and Variety manifest in cryptocurrency audits?
Volume Challenge:
Example: A crypto exchange processes 500K trades/day
Solution: Use blockchain explorers + data analytics tools
Variety Examples:
Structured: Trade timestamps, wallet addresses
Unstructured: Social media sentiment, support tickets
Audit Risk: Traditional sampling misses anomalies in large/diverse datasets.
Why are Velocity and Veracity critical for Crypto Audits
Velocity:
Crypto markets move 24/7 → auditors need real-time analysis tools
Example: Detecting wash trading during volatile periods
Veracity:
Blockchain is immutable, but extraction methods may introduce errors
Audit Procedure: Cross-check data across multiple blockchain explorers
How do Auditors derive value from big data
Benefits:
Fraud Detection: Identify suspicious transaction clusters
Risk Assessment: Correlate price swings with social media trends
Efficiency: Automate tests on 100% of transactions
Tools Used:
AI pattern recognition
Network analysis software
Example: Flagging transactions between “related” wallets disguised as independent actors
How does the IAASB define Data Analytics in Auditing?
“The science and art of discovering and analyzing patterns, deviations, and inconsistencies in data related to an audit through analysis, modeling, and visualization.”
Key Aspects:
Scientific: Uses statistical/mathematical techniques (e.g., regression analysis).
Artistic: Requires judgment to interpret results (e.g., assessing crypto transaction anomalies).
Outputs: Visualizations (e.g., network graphs of wallet interactions) inform audit planning/testing.
Example: Mapping Bitcoin transaction flows to identify potential related-party dealings.
How does Data Analytics Improve on the auditing quality
Deeper Entity Understanding - Analyse crypto transaction volumes/frequencies to assess business model risks.
Risk-Based Testing - Stratify data to focus on high-risk areas (e.g., wallets with abnormal trading volumes).
Enhanced Professional Scepticism - Flag deviations (e.g., transactions at non-market prices) for investigation.
Group Audit Consistency - Standardize analytics across subsidiaries holding digital assets.
Testing Complex Datasets - Audit 100% of blockchain transactions vs. impractical manual sampling.
Cryptocurrency Example: Using clustering algorithms to detect wash trading patterns.
How does the FRC define AI in Auditing
“The use of computer systems to perform tasks normally requiring human intelligence.”
Key Technologies:
Machine learning (ML), data mining, speech/image recognition, sentiment analysis.
What are the Five Principles of the UK government’s AI Framework
Safety, Security & Robustness - Ensure AI systems are resilient to attacks/errors.
Transparency & Explainability - Avoid “black box” models; auditors must understand AI decisions.
Fairness - Prevent bias (e.g., in risk-scoring algorithms).
Accountability & Governance - Clear ownership of AI tools’ outputs.
Contestability & Redress - Allow challenges to AI-driven findings.
How can Auditors leverage AI
Fraud Detection - ML analyses emails for suspicious language (e.g., urgency/evasion markers).
Risk Assessment - AI correlates financial data with news/social media for holistic risks.
Continuous Auditing - Real-time transaction monitoring (e.g., crypto exchange anomalies).
Efficiency Gains - Automate document review (e.g., extracting lease terms from contracts).
Outcome: Auditors focus on high-judgment areas.
What are the Key Challenges when integrating AI into audits
Data Integration - Merging structured (ledgers) and unstructured data (emails).
AI Hallucinations - False positives/negatives require human validation.
Skill Gaps - Auditors need ML literacy to interpret outputs.
Data Validation - Verify sources (e.g., blockchain vs. internal records).
Cost & Ethics - Balancing tool investments with confidentiality requirements.
Example: AI misclassifying legitimate crypto trades as “high risk.”
Why are auditors adopting AI and Big Data
Primary Driver: Client demands (businesses using AI → auditors must adapt).
Secondary Drivers:
Competitive Pressure - Firms using AI gain efficiency advantages.
Regulatory Expectations - Enhanced fraud detection capabilities.
Example: Auditing an AI-driven trading algorithm requires understanding its data inputs.
What is a Blockchain
A distributed ledger technology (DLT) that records transactions across a decentralized peer-to-peer network.
Uses cryptography to secure data, ensuring immutability and transparency.
What are the Key Features of a Blockchain
Decentralization -No central authority; maintained by multiple nodes (participants).
Consensus mechanisms (e.g., Proof of Work, Proof of Stake) validate transactions.
Immutability - Once recorded, transactions cannot be altered or deleted (tamper-resistant).
Transparency - All participants have access to the same ledger (varies by blockchain type).
Security - Cryptographic hashing ensures data integrity.
Example: Bitcoin’s blockchain records all BTC transactions since 2009.
