REDUCING ACCOUNTING ERRORS THROUGH INTELLIGENT AUTOMATION: A LITERATURE REVIEW AND PRACTICAL INSIGHTS

Abstract

The growing complexity of financial operations and increasing transaction volumes have made accuracy a key focus in accounting. Intelligent automation, incorporating technologies like Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML), has emerged as a significant solution to human errors in accounting processes. RPA automates repetitive tasks such as invoice processing and reconciliations, leading to a substantial decrease in errors. AI enhances RPA by enabling systems to process unstructured data and identify irregularities, improving auditing practices and fraud detection. ML further aids by analyzing historical data to predict financial trends and anomalies. This combination not only reduces errors but also enhances operational efficiency, allowing accounting professionals to focus on higher-value tasks. However, successful implementation of these technologies depends on the quality of data, proper system integration, and appropriate change management strategies. This paper reviews the literature and examines practical applications of intelligent automation in accounting, highlighting both its potential and challenges.