FULL PDF
Abstract
Investment advisors often fall prey to behavioral biases that negatively impact financialdecision-making. This study proposes and develops an AI-powered engine to detectbehavioral biasessuch as overconfidence, anchoring, and loss aversion—based onadvisors’ communications and portfolio decisions. Using machine learning modelstrained on historical advisory and market performance data, the engine flags biasconsistent patterns and provides corrective feedback. Our preliminary results indicatestrong predictive validity and real-time detection capacity. This innovation holdspromise for improving advisory outcomes and ensuring regulatory compliance
View more >>