Cognitive Bias Mitigation in Technology-Assisted Investment Decisions

Adrian Keller

Cognitive Bias Mitigation in Technology-Assisted Investment Decisions

Author(s)	Dr. Adrian Keller
Country	United States
Abstract	Investment decisions are rarely determined by information processing alone. Investors interpret market signals through cognitive and emotional mechanisms that may systematically distort judgments concerning expected return, risk, probability, market trends, and portfolio performance. Overconfidence, anchoring, confirmation bias, loss aversion, recency bias, disposition effects, and herding can consequently influence investment behavior even when extensive financial information is available. The rapid diffusion of robo-advisors, artificial intelligence, portfolio analytics, algorithmic screeners, automated rebalancing systems, and explainable financial models has created new opportunities to structure investment decisions more systematically. However, technological assistance should not be assumed to remove human bias automatically. Existing empirical evidence shows that behavioral biases can persist among robo-advisory users, while overconfidence may itself affect the decision to adopt automated financial advice. This study develops a technology-assisted framework for mitigating cognitive bias in investment decision-making through a structured integrative review of behavioral-finance, robo-advisory, human–algorithm interaction, and explainable-AI research. The analysis distinguishes between technologies that replace discretionary judgment and technologies that act as cognitive guardrails. Six debiasing mechanisms are identified: structured precommitment, benchmark-based comparison, counterfactual information exposure, portfolio-level risk visualization, explainable recommendations, and decision auditing with deliberate review intervals. The evidence suggests that effective technological debiasing requires preserving investor agency while making deviations from predetermined objectives visible and cognitively difficult to ignore. A published dataset concerning robo-advisory adoption is further visualized to demonstrate why technology adoption should not be interpreted as evidence of behavioral rationality. The paper proposes a Technology-Assisted Investment Debiasing Framework that combines investor-defined objectives, bias-sensitive diagnostics, independent evidence, algorithmic recommendations, human challenge, and post-decision learning. The study concludes that the most defensible role of investment technology is not to replace judgment completely but to create a disciplined decision environment in which intuitive judgments are repeatedly tested against evidence, predefined rules, diversification principles, and explicit uncertainty.
Keywords	: Cognitive Bias; Behavioral Finance; Investment Decision-Making; Robo-Advisors; Artificial Intelligence; Overconfidence; Loss Aversion; Algorithm Aversion; Explainable AI; Investor Behavior
Field	Engineering
Published In	Volume 1, Issue 5, September-October 2020
Published On	2020-10-26

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American Journal of Advanced Multidisciplinary Innovation and Research

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Cognitive Bias Mitigation in Technology-Assisted Investment Decisions

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