Optimising the design of financial data processing models in accounting information systems based on artificial intelligence techniques

Financial assessment and early warning analysis can help enterprises find potential financial problems earlier, make timely plans and take necessary measures to avoid risks. This paper uses a Bagging algorithm to integrate Random Forest, Support Vector Machine, and Plain Bayesian method to achieve the processing and classification of enterprise financial imbalance data. The entropy weight method is used to select and empower financial indicators to construct an accounting and financial data assessment model based on artificial intelligence technology. The model is applied to a consumer electronics enterprise, Company W, to analyze its financial situation and operating level. It is found that the composite score from 2019 to 2022 is 60.29, 70.80, 73.11, and 76.52, and the operating condition gradually improves from 2019. Debt service capacity, profitability, operating capacity, and growth capacity also show a positive trend. This is consistent with the actual development of Company W. Accordingly. It is recommended that Company W while maintaining its R&D advantages, focus more on the long-term operating ability of the enterprise, compress the operating cycle, reduce the risk of repayment and inventory pressure, and continue to enhance the competitiveness of the enterprise. This paper presents new ideas and methods for the innovation of enterprise management and the intelligence of accounting information systems.