Using AI for Audit Techniques

Syllabus: GS2/Government Policies & Interventions,  GS3/Science & Technology

In Context

• The Supreme Audit Institutions G20 conference emphasised the need for a common international audit framework relating to AI.

Artificial Intelligence

• It is the science and engineering of making intelligent machines, especially intelligent computer programs. 
• It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Significance  of AI

• AI would not replace people but create new opportunities in various fields. 
• It works on data, and if we could train our machines, it could do wonders for us in milliseconds by automating processes. 
• AI is creating new opportunities which could not be achieved by traditional technology.

AI for Auditing 

• In India:
  • According to the CAG, One Indian Audit and Accounts Department One System, a web-enabled IT application is going to support multiple languages, offline functionality, and a mobile app, enabling complete digitalisation of the audit process from April 1, 2023.
    • The CAG conducts financial audits, compliance audits, and performance audits.
  • This will perform with only one exception, the defence audit, because of security dimensions. 
  • The SAI G20 conference emphasised the need for a common international audit framework relating to AI.
• Global AI auditing frameworks:
  • Global organisations have developed many AI auditing frameworks. These include:
    • The COBIT framework for AI audit, the US Government Accountability Office framework, and 
    • The COSO ERM Framework. The U.K.’s Information Commissioner’s Office has published draft guidance on the AI auditing framework.

Significance

• Increased efficiency and accuracy: Audit Artificial intelligence implementation addresses the daunting task of sifting through vast amounts of data, automating tasks like data entry and analysis, leading to increased efficiency and precision.
  • This streamlining of the auditing process enhances accuracy and expedites operations, ultimately improving audit outcomes. 
• Increased reliability: One of AI audit software’s most significant contributions lies in its ability to provide deeper insights into complex data sets, uncovering valuable patterns and trends that enhance the reliability of audit reports. 
• Better scrutinization of irregularities: AI also plays a crucial role in detecting fraudulent activities, scrutinizing transactions, and alerting auditors to potential irregularities.
  • By producing detailed reports on suspicious activities, AI audit software empowers auditors to proactively address financial misconduct, ensuring greater integrity and compliance.

Challenges

• Alert on over dependence: The Comptroller and Auditor General of India (CAG), Girish Chandra Murmu, who is the chair for the Supreme Audit Institutions (SAIs) of the G20, warned that absolute dependence on Artificial Intelligence (AI) for auditing purposes may lead to inaccurate findings.
  • He also emphasised ethics as the cornerstone of responsible AI. 
  • According to him, the auditing challenges of AI include ensuring transparency, objectivity, fairness, and avoiding bias.
• Multiple sources & platforms: Since in India, the data for various government entities are taken from different sources and stored in multiple divergent platforms, the AI auditor will face enormous risks and challenges.
  • Audits cannot be based on big data from unauthorised sources. Data integration and cross-referencing become cumbersome. 
  • The data platforms of all entities must be synchronised through the government’s IT policies. 
• Potential for escalate existing threats: Generative AI is compounding or can compound some existing online threats like the use of deepfakes for disinformation campaigns.
  • There are multiple ways in which cheaper and more accessible generative AI models can compound issues that we’re still struggling to regulate, especially in cybersecurity and online harms.
  • These can threaten the basic foundations of democracy. 

Suggestions

• Responsible AI must be ethical and inclusive: Only ethical AI can add credibility, trust, and scalability to the CAG audit. Data sets must be complete, gathered on time, accurate, available, and relevant.
  • If integrity of the data fields is not ensured, we will have inaccurate audit findings. 
• Role of AI auditor: The AI auditor must be extra-vigilant about the risk of inherent AI data bias if data are taken from unauthorised sources like social media, where data manipulation and fabrication are common.
  • Since there is wide variance among AI systems and solutions, the auditor must adopt an appropriate AI design and architecture while defining the audit’s objective, scope, approach, criteria, and methodology. 
• Capacity building of auditors: There needs to be capacity building of auditors in varied aspects of the AI technology landscape so that they are familiar with AI frameworks, tools, and software. 
• Consultation with data specialists: AI audit assignments may require consultation with data scientists, data engineers, data architects, programmers, and AI specialists. 
• Data Protection Impact Assessments: Data Protection Impact Assessments are legally required if organisations use AI systems that process personal data to avoid potential risks.
  • The AI auditor must ensure that personal data is processed in a manner that guarantees appropriate levels of security.

Way Ahead

• With few frameworks available for auditing AI, auditors can only focus on the risks, controls and governance structures that are in place to determine whether they are operating effectively.
• AI is a powerful tool, but at the end of the day, we should be mindful of the fact that it is a means to an end and not an end in itself.