The use of artificial intelligence in Risk management

Risk management is becoming increasingly important in times of growing economic uncertainty. Efficient financial risk management can be decisive in identifying financial risks at an early stage, taking countermeasures and thus ensuring the continued existence of a company.

In our globalised world and a diverse intermeshing of effects and counter-effects, operational risks are becoming increasingly important. They have a direct impact on financial risk management, in particular on market risk, credit risk and liquidity risk. One example of this is the "Ever Given" disaster, which blocked the Suez Canal and impacted the global economy.

The use of modern technologies, in particular artificial intelligence (AI), opens up new opportunities to identify and manage risks more quickly. AI technologies are transforming treasury and Risk management and offer companies the opportunity to optimise processes and make them future-proof. Many companies are already aware of this. Our "AI in Finance" study shows that almost 50 per cent of the companies surveyed use AI to identify risks more quickly.

Financial risk management is not about avoiding risks, but about recognising them at an early stage and managing them in a targeted manner - AI can become a decisive factor here.

Nils A. Bothe

Partner, Financial Services, Finance & Treasury Management

KPMG AG Wirtschaftsprüfungsgesellschaft

auto_stories

Unsere Umfrage zeigt, in welchen Bereichen KI eingesetzt wird und welchen Einfluss das auf die Finanzberichterstattung hat.

Jetzt herunterladen

The potential of AI

AI offers significant potential for financial risk management, particularly in the following areas:

AI can analyse large amounts of data from various sources to identify risks faster and more precisely. By using machine learning and data mining, patterns and anomalies that indicate potential risks can be recognised. This enables more objective, faster and more accurate risk identification than with traditional methods.

Credit risks: AI assesses the creditworthiness of business partners and customers by analysing historical payment data and socio-demographic information.
Market risks: AI models analyse market trends to identify potential market risks such as price volatility and market liquidity.
Fraud risks: AI recognises unusual activities that indicate fraud by analysing transaction patterns.
Operational risks: AI identifies weaknesses in processes by recognising patterns.
Liquidity risks: AI analyses cash flows to predict potential liquidity bottlenecks.

AI can predict possible risks and assess their probability of occurrence and potential consequences. Predictive analytics uses historical data to forecast future events so that companies can take proactive measures.

AI can increase efficiency in Risk management by automating routine tasks. This allows risk managers to focus on more complex and strategic aspects.

AI can support compliance with legal and regulatory requirements by continuously monitoring and automatically generating reports. This reduces the risk of non-compliance and the associated financial and reputational damage.

Challenges and limitations in the use of AI in financial risk management

Although the use of artificial intelligence in financial risk management offers considerable advantages, it also brings specific challenges:

AI systems require high-quality data in order to be effective. Incomplete or incorrect data can lead to inaccurate predictions. The costs of improving quality and obtaining data should therefore be weighed against the potential benefits.

AI models can be complex and the results non-transparent. This makes it difficult to understand how decisions are made and to review and maintain the AI models. In Risk management, however, it is crucial that decision-making processes are comprehensible. If the causes and characteristics of a risk cannot be explained, an appropriate response is not possible.

AI models can be overly reliant on training data and therefore perform poorly with new data. This is problematic as market conditions can fluctuate quickly.

Too much dependence on AI systems can lead to human experts being less involved in the decision-making processes. This increases the risk of errors that can arise due to a lack of human judgement.

The analyses in financial risk management are based on highly sensitive data. It must be ensured that the data is protected against unauthorised access at all times and that compliance requirements are met. In addition, the ethical requirements of the EU AI Act must be taken into account.

Conclusion

The use of AI in financial risk management is promising, as the relevant risks are subject to certain logics and a good data basis is often available thanks to modern treasury management systems. The next steps for AI in treasury could therefore be as follows:

Ensure data quality: Checking and improving the quality and availability of data to create a solid basis for AI analyses.
Optimise human-AI interaction: Implement an efficient division of labour between risk managers and AI systems to automate routine activities and support complex analyses.
Conduct training courses: Train risk managers in the use of AI tools in order to fully utilise their potential and increase acceptance.
Ensure compliance and data protection: Ensure that all AI systems used comply with legal and regulatory requirements and that data is managed securely.

Ready to talk? Contact us.