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Artificial intelligence (AI) use cases are multifaceted and must be chosen carefully. In addition to technical issues, practical requirements for safety must be taken into account. 

Through the use of artificial intelligence, the most modern forms of automation are on their way to realising new efficiency potentials. While simple automation solutions, such as Robotic Process Automation, have been available on the market since the early 2000s, Machine Learning (ML) and Artificial Intelligence offer more far-reaching opportunities and optimisation potential. In particular, the functionality of "self-learning" offers a wealth of application fields to increase the quality of decisions in previously manual activities, even across interfaces. The use of AI in finance and accounting processes thus promises not only the automation of repetitive process steps, but also, in contrast to robotic process automation, the adoption of qualitative decisions that previously had to be made by humans.

The extensive and often complex functionalities of AI, especially its self-learning capability, also lead to higher demands on the corporate governance framework of companies. In order to achieve results of sufficient quality with the help of machine learning and artificial intelligence, AI-based algorithms must constantly learn, develop and change. This volatility poses new challenges for the control and monitoring of processes and IT systems. 

Also, the quality of the data used can lead to wrong decisions and rule violations. For example, AI algorithms can be programmed in a discriminatory manner or set up with biased test data in the context of checking the granting of loans to bank customers. As a result, certain customer groups (age groups, ethnic origin, etc.) could be treated in a discriminatory manner. This shows that the risks in dealing with AI are not only financial, but also affect the reputation of the company and raise ethical issues.

New, quality-assuring processes and controls must be implemented to ensure the stability of the AI systems and the traceability of the decisions made by them. 

KPMG supports companies in the efficient and secure use of artificial intelligence in the: 

  • Design and implementation of process and control structures in the IT and operational areas based on the KPMG AI in Control framework (link: global).
  • Design and implementation of an AI-oriented governance organisation with elements such as
  • Inventory and risk assessment
  • Training and communication measures
  • Establishment of trust (AI Code of Conduct, extended guidelines, system-side controls, quality standards, etc.)
  • Identification of potentials for automation with the help of artificial intelligence in your processes and for previous manual activities
  • Review of the governance organisation/internal control system via AI systems, the AI-specific operating model, as well as individual algorithms with reference to accounting (relevance in the context of the audit of financial statements). 

The KPMG AI Governance Team combines data scientists as well as process and governance experts with comprehensive knowledge in auditing and consulting on corporate governance with a focus on the use of digital solutions. Thus, we support the establishment and operation of a targeted AI environment that ensures secure and efficient AI operations and allows the full potential of automation solutions to be realised.

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