Risk management of accounting systems: Using artificial intelligence for real-time analysis and decision-making

The quality and dependability of financial data depend on risk management in accounting systems. Traditional risk management approaches often fail to provide real-time insights into emerging risks, potentially leading to significant financial losses. Real-time analysis and decision-making are the main focus, investigating how artificial intelligence (AI) could improve risk management in accounting systems. For the precise prediction of possible risks, including fraud, errors, and financial misstatements, in accounting data, the examination suggests a hybrid Enhanced Flower Pollination Algorithm tuned Gradient Boosting Machines (EFPA-GBM) model. The data was gathered, which included accounting records, financial transactions, and risk-related incidents. The data is preprocessed by handling missing values and normalizing them to ensure quality, followed by principal component analysis (PCA) for feature extraction. EFPA is utilized to improve the model’s parameters for increased accuracy, while GBM is utilized for risk prediction. This AI-driven model continually learns from data, which enables real-time risk analysis and decision-making in contrast to conventional rule-based systems. The performance metrics are evaluated by using Python software in terms of accuracy (94.2%), recall (94%), F1 score (92.9%), precision (93.8%), <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> (0.98), Mean Absolute Error (MAE) (0.42), and Root Mean Squared Error (RMSE) (0.6). The experimental findings show that, when it comes to detecting possible risks, the EFPA-GBM model performs better than other conventional risk prediction models. The suggested method improves decision-making and lowers financial vulnerabilities by improving the precision and promptness of risk management in accounting.