abstract

Financial decision-making is a complex process that cannot only be affected by rational consideration of the economic variables but also by behavioral biases and psychology like perceptions of risks, emotions, overconfidence, and heuristics. Conventional financial models that are mostly based on the rationality and market efficiency assumptions are not able to capture these human factors and therefore their predictive capabilities and feasibility of application are low. The paper will establish an AI-based mathematical modeling system that incorporates behavioral biases and psychological determinants in making financial decisions.

The research proposed to be conducted uses modern technologies, such as machine learning algorithms, artificial neural networks, and data-driven predictive analytics to simulate investor behavior more realistically. Behavioural constructs like loss aversion, herd behavior, anchoring, and emotional intelligence are measured and integrated into mathematical models, such as utility functions, probabilistic models, and stochastic models. Training and validation of the model is done using actual financial data of the world along with psychometric data that is gathered with the help of structured surveys and experiments.

It is supposed that the findings will prove that adding psychological aspects of AI-based financial models to the models enhances predicting investment behavior and investment performance. The study has added value to the new area of behavioral finance and financial technology (FinTech) by offering a solid interdisciplinary model that would close the gap in understanding between the human psychology and quantitative finance. Practical implications of the developed model on investors, financial advisors, and policymakers are that it makes informed and personalized financial decision-making and more psychologically conscious.