Data-Driven Financial Decision Making Using Machine Learning and Business Intelligence Tools

Increase in financial data along with the advancements in computational intelligence have shifted the approach towards financial decision-making from an intuitive one to a data-driven one. This research paper aims to explore the usage of machine learning (ML) algorithms in combination with business intelligence (BI) techniques in order to improve the process of financial decision-making. An innovative framework involving the use of gradient boosting algorithms for prediction purposes and BI dashboards for visualization and interpretation is suggested. The framework includes such steps as data preparation, feature extraction, training and deploying the models within the BI framework. The results of analyzing the loan and investment datasets show that the proposed framework allows reaching a prediction accuracy of 89% and reduces decision-making time by 39%.