綜言之，本研究釐清了性格特質在靜態與動態環境對風險決策的貢獻差異，並為金融機構建立具科學依據的心理分析框架。隨 AI 技術的普及，本研究主張將 MBTI 量化參數嵌入理財機器人演算法中，透過心理學與金融科技（FinTech）模型的整合，協助業者在強化 KYC 合規性的同時，能提供精準的理財建議與風險預警，實質優化投資精準度並提升客戶滿意度。;The MBTI personality trait theory has experienced explosive popularity in Taiwan in recent years. Its application has expanded from early social networking， corporate recruitment， and career planning to becoming a "common language" for interpreting human behavior. Behavioral finance has established a significant correlation between personality traits and risk preference; as a more universal personality classification tool， MBTI holds substantial research potential within the field of financial decision-making.
With Taiwan’s promotion of "Financial Inclusion" and "FinTech" transformation， the scale of Robo-Advisors has shown exponential growth since the relaxation of regulations in 2017. However， the "Know Your Customer (KYC)" investment risk profile assessments currently required by institutions under the Financial Consumer Protection Act and the Suitability Principle focus heavily on static indicators. These assessments struggle to capture dynamic psychological shifts and irrational behaviors during market volatility. This research aims to explore the correlation between MBTI personality traits triggered by dynamic events and investment risk profiles， seeking to strengthen the behavioral prediction capabilities of traditional KYC， which often lack psychological alignment.
This study employs a questionnaire survey method targeting the general public with investment experience， obtaining valid samples through convenience sampling. The research instrument adapts the MBTI psychological type scale from Chi (2021) with textual refinement， integrating it with an investment risk scale that simulates dynamic scenarios for empirical testing. For data analysis， SPSS statistical software is utilized to perform descriptive statistics， independent samples t-tests， one-way analysis of variance (ANOVA)， dummy variable regression analysis， and two-way analysis of covariance (ANCOVA) to validate the influence of demographic variables and the four primary MBTI dimensions on investment behavior.
Through empirical analysis， this study yields the following key findings: First， within the modern financial environment， gender behavioral tendencies regarding risk preferences demonstrate a converging trend; second， investment risk tolerance exhibits a non-linear relationship with age， reaching its peak within the 50–59 age bracket.
Furthermore， this study confirms that under a static baseline excluding external interference， the MBTI ′whole-personality configurations′ demonstrate an explanatory power (20%) for investment risk that is significantly superior to that of ′single psychological dimensions，′ validating that the whole personality is better suited to capture the predictive value of individual behaviors. When subjected to the interference of herding effects within digital social networks， empirical data reveal a robust interaction between whole-personality configurations and situational contexts， driving the overall explanatory power for investment risk behaviors up to 70.6%. In addition， this study implements a dual-track analytical strategy combining estimated marginal means (M) and standard errors (SE) to effectively eliminate the interference of outlier values stemming from small sample sizes. Through this approach， ESTP (the Entrepreneur) and ENFJ (the Protagonist) are specifically identified as ′high-risk bellwethers′ characterized by high group consensus， who are highly prone to exhibiting pronounced risk-escalating behaviors catalyzed by herding effects. Evidently， personality traits serve as a core variable in predicting dynamic decision-making. Lastly， this study specifically identifies that the Judging (J) / Perceiving (P) dimension exhibits a significant interaction under herding pressure， and the high-order interaction of EI × SN × JP × herding behavior is found to be extremely significant (F= 14.036， p < .001).
In summary， this study clarifies the differential contributions of personality traits to risk decision-making across static and dynamic environments， establishing a scientifically grounded psychological analysis framework for financial institutions. With the ubiquity of AI technology， this research advocates for converting MBTI dimensions into quantitative parameters to be embedded within robo-advisor algorithms. Through the integration of psychology and FinTech models， financial practitioners can enhance KYC compliance while providing precise investment advice and risk warnings， ultimately optimizing investment accuracy and elevating customer satisfaction.
