Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?

Abstract

The main novelty of this paper is proposing artificial intelligence (AI) to manage behavioural biases in the financial decision-making process. An empirical study by Kahneman and Tversky identifies the evidence of behavioural biases in the investment decision-making process: a reversal of an established tenet in traditional finance. Financial planners are vulnerable to behavioural biases and are therefore unable to provide optimal investment solutions for their clients. Identifying the limitations of current practice, this research attempts to address how AI can help financial planners in subduing their behavioural biases and proposes the adoption of AI in financial planning services to circumvent behavioural biases. In recent years, AI has attained significant efficacy and has proven to be efficacious through supervised and unsupervised learning. Applying these AI techniques in mitigating behavioural biases, this study confirms that the backpropagation within the neural network and deep reinforcement learning can help overcome confirmation and hindsight biases.

Transforming Financial Planning With Ai-Driven Analysis: A Review and Application Insights

“…This study points to the personalized nature of AI applications, which can tailor financial advice and planning to individual needs, thereby enhancing the overall efficacy of financial planning processes. Hasan et al (2023) explore a more specific application of AI in financial planning, focusing on the management of behavioral biases among financial planners. Their research suggests that through techniques such as backpropagation within neural networks and deep reinforcement learning, AI can mitigate biases like confirmation and hindsight biases, often hindering optimal financial decision-making.…”

“…Choi, Choi, and Hong's (2023) work on older adults' financial concerns suggests that while AI has the potential to significantly benefit this demographic, there remains a gap in the design and deployment of AI tools that cater to their specific needs. In addressing behavioral biases, Hasan et al (2023) highlight a critical area where AI can augment human expertise in financial planning. By providing tools that help financial planners overcome cognitive biases, AI enhances the objectivity and rationality of financial decisions, which is paramount for the efficacy of financial planning.…”

“…However, despite its numerous advantages, the application of AI in financial planning is not without its limitations and drawbacks. Hasan et al (2023) highlight a critical aspect of AI's limitations in managing behavioural biases among financial planners. While AI can mitigate biases such as confirmation and hindsight biases through backpropagation within neural networks and deep reinforcement learning, its effectiveness is contingent upon the quality of data and the complexity of financial environments.…”

The Intersection of Ai and Quantum Computing in Financial Markets: A Critical Review

“…Successful implementations of AI and quantum computing in financial markets have been documented in various studies. For instance, AI has been utilized to manage behavioral biases among financial planners, with techniques such as backpropagation within neural networks and deep reinforcement learning proving effective in overcoming biases (Hasan et al, 2023). Additionally, AI has been successfully employed to analyze stock market activities, demonstrating its potential to contribute to decision-making processes in financial markets (Song & Jain, 2022).…”

Transforming financial planning with AI-driven analysis: A review and application insights

“…However, despite its numerous advantages, the application of AI in financial planning is not without its limitations and drawbacks. Hasan et al (2022) highlight a critical aspect of AI's limitations in managing behavioural biases among financial planners. While AI can mitigate biases such as confirmation and hindsight biases through backpropagation within neural networks and deep reinforcement learning, its effectiveness is contingent upon the quality of data and the complexity of financial environments.…”