Machine learning for financial auditing and risk management in modern enterprises

Amid rising corporate fraud, growing investor skepticism, and intensifying regulatory pressure, financial audits have become a critical line of defense for enterprises worldwide. Yet traditional manual audit methods often fail to detect deep, systemic risks hidden within complex enterprise data. As businesses scale and data explodes, the need for intelligent, automated risk detection is no longer optional - it is urgent.

To address this gap, a new study titled “Machine Learning based Enterprise Financial Audit Framework and High Risk Identification”, published on arXiv, introduces an enterprise-ready framework that leverages artificial intelligence to identify high-risk financial activity. The research benchmarks machine learning models against real-world audit data from the Big Four accounting firms, EY, Deloitte, PwC, and KPMG, spanning the years 2020 to 2025.

How do machine learning models enhance risk detection?

The study seeks to determine which machine learning models best identify financial risks, compliance violations, and potential fraud cases. The researchers evaluated three algorithms, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), on a meticulously preprocessed dataset that included details such as audit engagement volumes, fraud occurrences, violation histories, employee workload, and client satisfaction levels.

Among the three models tested, the Random Forest algorithm emerged as the most effective. It demonstrated superior performance metrics, including an F1-score above 0.90 and an accuracy of over 92%, outperforming the other two algorithms in predicting high-risk audit scenarios. The RF model excelled in handling the non-linear, high-dimensional nature of financial audit data, making it particularly suited for enterprise-level deployment.

SVM also showed solid results, especially in environments where the dataset's dimensionality was high, but its computational expense limited its scalability. The KNN model, while relatively less effective in precision, gained improved performance with the integration of SMOTE (Synthetic Minority Over-sampling Technique) to address data imbalance. However, KNN remained more sensitive to noisy datasets, which could affect its real-world robustness.

The models were assessed on their ability to flag high-risk events across firms and industries. Notably, PwC and Deloitte experienced significant upticks in flagged high-risk cases during 2022 and 2024, respectively, signaling variations in auditing practices or risk exposure trends over time.

What factors influence high-risk identification?

Beyond identifying which algorithms work best, the study explored which variables most strongly correlate with financial risk. Several critical factors emerged, including audit frequency, historical violation records, average employee workload, and client satisfaction scores.

A moderate to strong correlation was detected between the number of historical violations and the percentage of risk events in recent audits. The analysis showed that firms with a higher history of non-compliance were more likely to face new risks, indicating a compounding effect in audit risk evolution. Additionally, heavy employee workload was found to be a leading indicator of risk exposure, suggesting that overburdened auditors may inadvertently miss red flags.

Client satisfaction, often regarded as a peripheral metric, was found to hold predictive value. High satisfaction levels were positively associated with reduced risk percentages, hinting at the role of ethical and transparent business practices in mitigating audit threats. Conversely, spikes in risk percentages typically accompanied low satisfaction levels and increased regulatory violations.

The research also discovered an inverse correlation between total audit engagements and observed risk under manual auditing frameworks. This implied that while more audits might dilute focused risk detection, machine learning models like Random Forest and KNN were able to maintain or even improve risk identification under high audit volumes.

How can the framework be applied in real-world auditing?

The authors suggest the adoption of Random Forest as the core of enterprise audit risk management systems due to its reliability and scalability. It is particularly well-suited for large corporations that generate extensive audit trails across business units and time frames.

However, the study does not ignore the limitations of its approach. The dataset primarily consisted of structured and static entries, lacking integration with unstructured data sources such as textual audit notes, financial news, or whistleblower disclosures. The researchers acknowledge that risk detection accuracy could be significantly enhanced by incorporating deep learning models and alternative data types in future studies.

Additionally, the study did not factor in macroeconomic indicators, market volatility, or sectoral regulatory changes, which are crucial variables in understanding financial behavior in context. Future directions include the use of federated learning to enable privacy-preserving collaboration between auditing firms and the deployment of graph neural networks to model enterprise relationships and transaction chains more holistically.

Data ethics and transparency are equally important when using machine learning for audit decisions. While the models are effective in detecting statistical anomalies, human oversight remains vital for final decision-making, especially in high-stakes regulatory or legal contexts.