Automated Accounting Reconciliation using Generative AI: Accuracy, Auditability, and Internal Control Risks

Abstract

The implications for the use of generative artificial intelligence (AI) in automated accounting reconciliation, such as whether the technology is accurate, auditable, and poses any internal control issues, are discussed. Traditionally, banks have been required to perform a significant amount of manual tasks, including comparing bank statements, invoices, ledger and receipts and payment records, which can be time consuming and prone to errors. Generative AI can match transactions, summarize exceptions, provide explanations for unmatched transactions and classify exceptions in a quicker and more flexible manner. The study explores the benefits of AI tools when it comes to improving the accuracy of matching, processing speed, duplicate payment detection, missing payment detection, timing difference detection and unusual transactions assistance for the finance team. With the integration of structured data, clear rules, and human oversight, the outcomes indicate that generative AI could be a valuable addition to improving the effectiveness of reconciliation, as well as providing valuable insight into accounting exceptions. In addition, the study highlights the major issues related to mismatched matching, poor audit trails, reliance on AI-generated explanations, data privacy concerns, and potential loss of control. The paper concludes that generative AI is an "assistive tool" and should not be the sole decision maker when it comes to accounting decisions. Robust internal control, approval processes, transparent output explanations, access and control and periodic audit oversight are critical features of AI's reliable and accountable accounting reconciliation application.