Released
The New Skills in Finance Report 2022
In a digital-transforming era, there is a widening skills gap for those who cannot adapt to the new digital world in finance.
CFTE and Elevandi published the report with the discussion with leading experts to help governments, organisations and individuals address the current skills gap in finance and build a digital-resilient workforce in the industry.
Key Insights
Key takeaways from "Responsible AI and the challenge of AI risk" report published by KPMG
CFTE summarised “Responsible AI and the challenge of AI risk” report by KPMG. This report sheds light on executives executives across multiple sectors of risks associated with AI and predictive analytics models and the challenges they face in addressing them.
Key Aspects
- Major focus areas include understanding AI and predictive model risks, implementing effective risk mitigation strategies, and the necessity of developing robust AI risk management functions within organizations.
Table of Contents
- Introduction: The Challenge of AI Risk
- Key Stats and Findings
- Identifying and Managing AI Risks
- Risk Mitigation Strategies
- Research Methodology
- Detailed Analysis of AI Model Risks
- Managing AI Risks: Roles and Responsibilities
- Future Outlook: Oversight and Regulations
- Addressing Risks through Responsible AI
- How KPMG Can Help
Key Findings and Insights
This report will give you an insight into:
- Understanding of AI and Predictive Models:
◦ The report reveals that a majority (82%) of organizations surveyed have a clear understanding of AI and predictive analytics models. However, it notes that traditional sectors like IM & ENRC still need to build more clarity.
◦ This insight underscores the varying levels of AI adoption and understanding across different industry sectors. - Regulatory Oversight and Limiting Factors:
◦ Approximately 73% of respondents acknowledge some degree of regulatory oversight over predictive models.
◦ The main challenges identified are a lack of skilled resources, budget constraints, and limited tools, which are significant barriers in the risk review process of AI models. - Future Expectations and Audit Requirements:
◦ A significant 85% of respondents anticipate an increase in the use of AI and predictive analytics models, and 84% believe that auditing these models will become a requirement within the next 1-4 years.
◦ This reflects a growing awareness of the need for oversight and accountability in AI implementations. - Risk Management in AI Models:
◦ Data integrity, statistical validity, and model accuracy are highlighted as the top three risks that businesses are actively managing or mitigating.
◦ The prioritisation of these risks suggests a focus on the foundational elements of AI model reliability and effectiveness. - AI Risk Mitigation Strategies:
◦ 39% of respondents are very likely to invest in rapid diagnostic tools to assess AI model risks, preferring subscription-based services due to the high cost of these tools.
◦ Additionally, 66% of firms without a formalized AI risk management function aim to establish one in the next 1-4 years, indicating a shift towards more structured risk management approaches. - Cascading Risks and Responsible AI: The report highlights the complexities of AI risks, including cascading errors in chained AI models, and emphasizes the need for a responsible AI program encompassing fairness, explainability, accountability, and more
Key infographics
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