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Audit
Technology Slides
| Question | Answer |
|---|---|
| ways tech is making audit more efficient | - automated working papers -> improve internal processes - cloud-based client spaces for data transfer - robotic process automation - drones - audit data analytics |
| more effective audits | audit data analytics, artificial intelligence |
| more technology could | increase costs, therefore widen gap between large and small firms |
| robotic process automation | - replaces repetitive, routine, manual tasks - needs to be established process - only suitable for structured data - not suitable for areas requiring judgement |
| IAASB audit data analytics definition | the science and art of discovering and analysing patterns, deviations, inconsistencies and identifying anomalies in data underlying or related to subject of an audit through analysis, modelling and utilisation for purpose of planning or performing audit |
| what testing is audit data analytics used for | journal and transaction |
| how does ADA increase reliability | testing all data |
| ADA - need client permission for | using their information - data security concerns |
| 4 main considerations regarding tech and audit | - move at client pace or lead - invest? buy? wait? - standards / quality / competition - divergence within market (investment capabilities big 4 v small) |
| benefits of technology | - productivity - profitability - marketing advantage - hindsight -> insight -> foresight - quality and value for the client increases - recruitment and retention advantages |
| FRC view on the quality impact technology has | - reduced detection risk (representative sample or whole population - better understanding of clients systems and controls - time freed up to focus on judgement - improved communication and connectivity |
| transitional issues | composition of workforce |
| staffing issues | recruitment and retention |
| fees issues | absorb, recharge, retain |
| competition issues | market barriers and differentiation |
| client issues | expectation, data suitability and protection |
| technical issues | security, transparency, innovation, reliability, obsolescence |
| quality issues | fraud, better evidence, ISA compliance, regulatory compliance |
| ethics issues | independence, professional competence |
| artificial intelligence | use of computer systems to perform tasks normally requiring human intelligence |
| assisted AI | supports humans in decision making or taking action - human in charge |
| augmented AI | supplements human decision making and learns from human and their environmental interactions - humans and AI share decision making - more powerful |
| autonomous AI | acts independently, no human intervention - AI in charge and exhibits intuitive / empathetic intelligence - no human input, wholly independent |
| ethics issues - fairness | include diverse perspectives in design |
| ethics issues - accountability | responsibility is clear |
| ethics issues - sustainability | avoids harm to individuals and otheres |
| ethics issues - transparency | clear disclosure where AI used |
| ethics issues - human oversight | human ability to monitor and change is needed |
| ethics issues - data | data privacy embedded throughout |
| ethics issues - safety and robustness | backup plans in place should operation fail |
| ethics issues - standards and law | lawful actions and compliance with emerging regulation |
| examples of AI regulation | EU AI Act 2024 and AI Assurance and Governance 2024 |
| EU AI Act 2024 | - prohibits AI for certain processes - prohibited issues have the potential to do social harm - not affecting the UK |
| AI Assurance and Governance 2024 | 5 principles - safety, security and robustness - appropriate transparency and explainability - fairness - accountability and governance - contestability and reassess |
Created by:
amyperston
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