Analyzing Behavioral Patterns in Advisor Recommendations Using Machine Learning Techniques

Le résumé fourni par la source

The study introduces a new machine learning paradigm that the author uses to identify and illuminate the behavioral background in the financial advisor recommendations. The strategy is aimed at examining the compatibility of advisor practices, which are either conservative, neutral, or aggressive, with client necessities and regulatory requirements. The system uses a historical data of recommendations with preprocessing, feature extraction in terms of behavior and the use of supervised classification to model advisor tendencies. A time analysis element also tunes behavior tracking by time. Experimental analysis proves that the proposed model performs better with an accuracy of 96.4%, precision of 95.7 %, recall of 94.9 %, and$\mathbf{F 1}$-score of 95.3, compared with baseline models, including Random Forest, SVM and XGBoost. The suggested system also has a low Mean Absolute Percentage Error (MAPE) of$\mathbf{8. 4 \%}$that is in line with good predictive capability. Confusion matrix proves high classification assurance in all categories of behaviors. Such a framework will provide the firms with a scalable approach to tracking advisor behaviors, enforcing compliance, and maximizing client-advisor alignment using real-world datadriven methods in financial advisory frameworks.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.