Artificial intelligence Part 7: specific industry impacts - corporate governance and risk management

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The impact of artificial intelligence on governance and risk management is nuanced. There are some tasks that AI can do, but other functions must be undertaken by human beings. As a general description of work functions, governance teams usually produce documentation such as board briefing papers, governance reports, policy updates, compliance documentation and meeting summariess. These are all processes open to AI. This posting will provide an indication of changes coming to roles in corporate governance and risk management due to AI. 

Corporate Governance

• Board reporting and briefing papers preparation: AI can synthesise management informaiton, financial data and identify risks into baord-ready material reducing the administrative teams that currently do this work.
• Corporate secretariat functions: the various documents produced such as records of minutes, compliance tracking, regulatory filings are all highly structured functions that are automatable and suited to AI.
• ESG reporting: Environmental, Social and Governance (ESG) reporting requirements have increased in recent years creating an additional compliance burden. AI can offset the increase through tools that aggregate and report sustainability data.

Risk Management

• Credit risk modelling: Credit risk modelling is a heavily quantitative task suited to AI thus reducing the analyst layers that do the manual model running and reporting. 
• Operational risk assessment: risk assessment identifies and quantifies risk across various buinsess processes. The assessment often produces a risk assessment table which rates and grades the risk including mitigation method. These processes are suited to be augmented by AI at the data gathering and first-analysis stage.
• Market risk monitoring: real-time AI surveillance is replacing/reducing some human monitor headcounts for this task.

Compliance and regulation

• KYC (Know Your Customer) and Anti-Money Laundering (AML): AI is transforming these tasks from labour-intensive manual processes to AI-monitored exception-handling workflows. The impact is the reduction in the size of large compliance teams. 
• Regulatory change management: the tracking and interpreting of new regulations can be augmented by AI formonitoring ans summarising only. The interpretation of ambiguous regulatory language remains an essential human function not AI.
• Audit: the largest 4 accounting firms are deploying AI to analyse entire transation populations rather than the traditional scope of only sampling. This change provides a better quality audit but it requires fewer junior auditors.

Prudential Regulation (Central Banks and Regulators)

• Supervisory data anlysis: regulators such as APRA (in Australia), FCA (US) and Central Banks are using AI to monitor systemic risk across institutional data.
• Examination and inspection of institutions: the inspection teams face efficiency improvements and possibly headcount pressure.
• Policy and rule-making: roles in these functions are more protected however the judgement and accountability requires is high for human decision making.

In terms of employment, senior risk professionals are still needed for a range of actions such as - determining acceptable risk levels, advising executives on risk management, assessing complex of emerging risks, balancing regulatory, financial and reputational considerations. 

Physical inspection teams are still needed and remain human-based for purposes such as: construction safety inspections, infrastructure maintenance checks, environmental site visits, equipment integrity inspections. 

In the future, the workforce changes are more likely to be a reduction in the entry-level and junior operational roles with a structure as shown -

Before
Chief risk/governance officer

Senior specialists

Large analyst and reporting teams

After

Chief risk/governance officer

Senior specialists (fewer)

Small team supervising AI monitoring systems

The AI systems in use include: Microsoft Copilot, IBM Watson, Palantir Technologies and SAS Institute.