Automating Financial Audits with Random Forests and Real-Time Stream Processing: A Case Study on Efficiency and Risk Detection

Abstract

In the current complex economic environment, enterprises are increasingly in need of efficient, accurate and real-time financial audits. Traditional audit methods are difficult to cope with the challenges brought by massive data and dynamic risks. This paper explores the automation method of financial audits based on artificial intelligence in depth, aiming to improve audit efficiency and risk identification capabilities. The study introduces the random forest algorithm, constructs 100 decision trees, self-samples data from the training set, and randomly selects features at each node for splitting to reduce the overfitting risk of a single decision tree and improve the generalization ability of the model. At the same time, with the help of real-time data processing platforms such as Kafka and Blink, real-time collection, processing and analysis of financial data are achieved to ensure the timeliness and dynamism of the audit process. After a series of steps, including extracting 500 features from multi-source data, dividing the data set containing 5,000 records into 70% training set and 30% test set, the model is trained and evaluated. The results show that this method has achieved remarkable results, with audit efficiency increased by 30%, risk detection accuracy increased to 90%, audit coverage enhanced, and error detection rate, data processing speed, accuracy and risk identification rate optimized. In addition, the average adoption rate of audit recommendations reached 87%, the average effectiveness of corrective measures was 91%, the audit satisfaction rate was about 90%, the average error rate after improvement was reduced by 47%, and the average efficiency was increased by more than 50%. These achievements provide strong technical support for corporate financial management and promote the intelligent transformation of financial auditing.