A Big Data-Driven Approach to Financial Analysis and Decision Support System Design

Abstract

This study aims to design and implement a financial data analysis and decision support system leveraging big data to enhance financial management and decision-making capabilities in complex market environments. Through a detailed examination of Company A's financial data, key indicators such as sales revenue, cost of sales, net profit, current assets, current liabilities, total assets, and shareholders' equity are selected. Utilizing big data technology, the system achieves efficient data processing, precise financial risk early warning, and scientifically informed investment decision support. Using Hadoop and Spark, the system efficiently processes extensive financial data while monitoring key indicators, such as sales revenue, cost of sales, and profitability. Specific financial models, including net present value (NPV) and internal rate of return (IRR), were tested for their effectiveness in supporting optimal investment decisions, yielding an NPV of 5.12 million and an IRR of 16.8% in the best-performing scenario. Performance metrics indicate a consistent improvement in data processing speed, accuracy reaching 98.9%, and user satisfaction rising from 8.2 to 8.7 over three years. The results indicate that the system performs effectively in terms of data processing speed, accuracy, and user satisfaction, enhancing both the efficiency of financial management and the precision of decision-making processes within enterprises. The financial risk early warning system successfully identifies potential risks in a timely manner, while the investment decision support system aids enterprises in selecting the optimal investment strategy by utilizing indicators such as net present value, internal rate of return, and investment payback period. This study highlights the value of applying big data technology in financial management, offering robust support for enterprises aiming for stable development within a rapidly evolving market environment. Future research will focus on further optimizing system functionality and expanding application scenarios to adapt to the shifting financial management demands of enterprises.

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