Financial industry risk-return management by AI Agents

Peter Haenni

• 1 min read
Financial industry risk-return management by AI Agents
SIPOC process map auto generated by the ProcessHorizon web app

Goals set in the AI objective functions directly shape and govern the AI risk-return management process in the financial industry.

The objective function in AI risk-return management should seek to balance risks & returns, ensure regulatory compliance and optimize portfolio performance in alignment with stakeholders' goals.

Key Objective Functions in the AI risk-return management process steps (defined as Subprocesses):

  • Risk Assessment: The objective function here is to minimize risk exposure while maximizing returns within acceptable limits. This drives the identification and measurement of financial risks (e.g. market risk, credit risk) and ensures that portfolios are aligned with the institution’s overall risk tolerance.
  • Portfolio Optimization: The objective is to maximize return for a given level of risk, ensuring that assets are allocated efficiently to meet the risk-return preferences of investors.
  • Stress Testing: The objective is to minimize financial instability by ensuring that the portfolio performs well under extreme scenarios, maintaining resilience even during market downturns.
  • Compliance Verification: This step is driven by the objective to ensure compliance with regulatory requirements, avoiding penalties and ensuring that the portfolio aligns with the legal standards set by regulatory bodies.
  • Risk Reporting: The objective here is to maximize transparency and ensure that risk exposure is reported clearly and accurately to stakeholders, keeping them informed about the risk status of portfolios and overall investments.

Strategies to mitigate unwanted or unintended outcomes:

  • Stakeholder Consultation: Involve all stakeholders in defining and reviewing the objective function to ensure alignment with real-world outcomes.
  • Holistic Approach: Avoid narrow optimization; incorporate social, ethical, and environmental considerations into the AI's decision-making.
  • Continuous Feedback: Regularly review the objective function’s performance and make adjustments based on stakeholder feedback to avoid unintended negative outcomes.

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