Predictive Analytics for Sustainable Investment Portfolios in Banking

Purpose: The main purpose of the study is to generate an idea about the validity of data-driven predictive analytics related to sustainable banking investment portfolio development. Methodology: Depending on the positivism philosophy, the researcher has focused on generating a hypothesis that can be evaluative and generate an outcome related to the topic under discussion. The descriptive design of the study has helped the researcher develop an idea about the different perspectives related to sustainable investment and ESG goals. The primary quantitative data analysis is conducted by purposefully generating a sample of 101 participants, coming from the managerial level dignitaries from 10 leading banking sectors of the European continental financial market. Results: Analysing the opinions given by the respondents by IBM SPSS software, the results of the study. The results show that most of the male participants have shown their interest in participating in the survey which indicates the career growth of male bankers to be higher than the female ones. The most participating age group is found to be the people belonging to the age group of 21 to 35. The mean value of the correlation coefficient is found to be 0.921 and the mean value of the coefficient of determination is found to be 0.848. On the other hand, the regressive degree of freedom is found to be 11 which indicates the linear relationship present within different variables that are considered generally by the respondents. Conclusion: The interdependence of the variables clearly shows that the market trend regarding the future of the sustainable investment sector present within the banking field can be developed based on predictive analytics inclusion within the level of investment that is focused on meeting the ESG goals for improving sustainability. These factors act as the main motivators for the cross-departmental teams that act simultaneously for performing other tasks along with maintenance of predictive modelling of real-time data to evaluate the future of the banking investment sector.