Embedding behavioral biases into robo-advisory platforms-case of UAE investors

Purpose: This study aims to identify individuals' biases while making investment decisions and explore how these biases can be incorporated into a robo-advisory platform to help mitigate these biases. This paper identifies eight investment-related behavioral biases: mental accounting, gambler’s fallacy, hindsight, regret aversion, disposition, trend-chasing, loss aversion and herding. Design/methodology/approach: This study uses primary data from 263 respondents across various age groups, of which approximately 50 were wealth management professionals in the UAE. A random sampling method from probability sampling is employed to gather the primary data. The identified biases serve as dependent variables; the age and income of individuals serve as the independent variables. Findings: Age and income are significantly related to mental accounting, herding, gambler fallacy and loss aversion. Existing studies on behavioral finance demonstrate that individuals who make investment decisions are susceptible to cognitive fallacies, leading to nonrational investment decisions. Practical implications: By studying these biases affecting individuals of varying ages and income levels, wealth management professionals can tailor their financial robo-advisory services to address these biases and help clients build wealth with consistent investment. Originality/value: This study uses survey-based sampling in the context of the UAE; hence, the data and analysis represent originality.