Achieving Automated Reconciliation of Financial Records via Artificial Intelligence: Reducing Errors and Time Costs for U.S. Financial Service Providers

Studies have found that US financial service providers often use manual financial reconciliation methods due to the fact that their business model is error-prone and inefficient. The average error rate can be more than 3%, the cost for completing a transaction is 2-3 hours, and when the transaction quantity grows larger, there will appear large quantity errors and increased costs, making it hard for them to meet regulatory compliance and efficient business operations. The study seeks ways to resolve these issues. In terms of automating the reconciliation of financial records (including bank statements and accounting vouchers), research on the effect of AI is conducted using desensitized data from three US financial institutions (from 2023-2024). In these analyses it has been shown that the AI reduces the rate of errors to 0.4%, decreases the time per transaction from 16 min to 8 min and increases the compliance match rate from 98.5% to 99.2%. In short, this study adds further validation to and clearly demonstrates the fact that the use of AI will quantifiably augment and accelerate AI's“technology application-effect”on financial reconciliation and provides such a solution that is applied, feasible and executable for both institutions and their internal personnel, in addition to improving institution operations and governance, ensuring compliance with standards, etc.