LLMs are becoming part of investment research, portfolio analysis, risk management, and client service. Their speed and scale can improve productivity, but biased inputs, model behavior, and workflow decisions can also distort recommendations, amplify errors, and create financial, regulatory, ethical, and reputational risks.
“Managing LLM Bias in Investing: From Detection to Mitigation” explores how bias can influence AI-assisted investment decisions. It examines common human biases, such as availability, anchoring, framing, and positional and self-preference bias, and explains how these can interact with AI prompts, selected information, system instructions, model design, and AI systems that make decisions or take actions during a workflow (agentic AI workflows) to reinforce biased outcomes.
The report combines behavioral finance with original experimental research to help firms build more transparent and reliable AI-enabled investment processes. It distinguishes implicit LLM bias, which arises from pre-training data, model architecture, and training procedures, from explicit LLM bias, which appears in observable choices such as data selection, source use, and analytical steps.
This distinction shifts attention from whether a model is simply “biased” to how a complete investment workflow produces its result. That broader view helps firms locate the source of a problem, select an appropriate control, and assign responsibility for reviewing the final decision.
