Rethinking Soft Skills for Business Analytics in the Predictive Financial World

Abstract

The aggressive dependence on predictive analytics, a crucial tool for transforming financial decision-making, compels an essential balance between technical expertise and human soft skills, including critical thinking, emotional intelligence, ethical judgment, and communication. Existing research recognizes the significance of soft skills in data science and business analytics, but lacks a specific focus on the financial sector. This study aims to bridge that gap by exploring how soft skills enhance predictive financial analytics, ensuring ethical, transparent, and effective decision-making in the financial world. The research objectives are to identify key soft skills for financial analysts, examine their impact on financial decision-making and risk assessment, and project a structural framework for their development and incorporation with technical expertise to reduce risks. The study proposes a mixed-methods approach, combining qualitative data from interviews and focus groups with quantitative analysis from surveys and behavioral experiments. Data will be analyzed using Statistical Correlation, Regression Analysis, and Machine Learning Models. This research is expected to deliver an empirical framework linking soft skills to predictive financial decision-making, providing insights into how adaptability, communication, and intuition impact forecasting accuracy, and offering strategies for developing well-rounded financial analysts in an AI-driven world.