This systematic literatureHoan Nguyen Thi ThuNgoc Nguyen Bao review aims to explore the application of big data analytics in financial decision-making within companies, with a focus on investment and financing decisions. The review followed specific inclusion criteria to target only articles that are English, accessible, up-to-date, and relevant to the application of BDA, AI, ML, DL, or NN in financial decision-making at the firm level. Based on the PRISMA 2020 guidelines for systematic reviews, the study identified 48 studies out of 6,102 initial records found on Scopus, following study identification, screening, and eligibility checks, ensuring the broad coverage of BDA’s applications in investment and financing decisions of companies. Among these, 92.9% of studies highlighted BDA’s transformative role in enhancing investment decisions through its capabilities in improved credit risk evaluation, risk management, investment analysis, fraud detection, demand forecasting, inventory management, and working capital management, as well as financing decisions. Numerous studies consistently showed that investment and financing decisions of companies are facilitated by the adoption of BDA along with AI, ML, DL, and NN techniques. Limitations include methodological variability across studies and the lack of detailed datasets, making generalization challenging. Despite this, the findings emphasize BDA’s potential to enhance financial decision-making, enabling firms to balance growth and operational stability. Future research should address biases and explore more specific applications of BDA in finance.