AI-Powered Continuous Financial Reporting: Integrating Machine Learning Anomaly Detection with Blockchain-Immutable Audit Trails
0 viewsAbstract
This study looks at whether combining machine learning-based anomaly detection with a permissioned blockchain ledger can make financial reporting faster, more accurate, better at catching fraud, and more efficient than the traditional manual approach. I ran a comparative case study in a mid-sized online retailer, operating two systems side by side for three months: a conventional manual reporting process and an integrated AI–blockchain platform. I analyzed about 10,000 transactions across five metrics: monthly closing duration, transaction error rate, quarterly audit hours, fraud detection accuracy, and monthly reporting cost. The technology-enabled system showed clear gains. Closing time fell from 6 days to 2 days—a drop of 66.7%. Transaction errors declined from 4.2% to 1.1%, a reduction of 73.8%. Audit hours went from 140 down to 70, a decrease of 50.0%. Fraud detection accuracy rose by 30 percentage points, climbing from 58% to 88%. Monthly reporting costs also dropped by 37.5%, from $20,000 to $12,500. These results suggest that an AI–blockchain system can shift financial reporting from a periodic, retrospective exercise into a more real-time, evidence-based process, which could strengthen internal controls and reduce agency costs. The study's limitations include its single-case design, the exclusion of implementation costs, and the absence of inferential statistics. Future research should replicate this work in other industries and examine how regulators might adapt IFRS and US GAAP.
Keywords
Accounting Automation, Artificial Intelligence, Blockchain, Continuous Auditing, Financial Reporting, Fraud Detection