AI-Powered Revenue Recognition Models In Multi-Platform Ad Ecosystems
DOI:
https://doi.org/10.29284/8hm66v67Keywords:
Artificial Intelligence; Revenue Recognition; Digital Advertising; Multi-Platform Ecosystems; Accounting Information Systems; Predictive Modelling; Financial ReportingAbstract
The recognition of revenues in digital advertisement has gotten more complicated as a result of the expansion of multi-platform ad ecosystems, performance-based strategies of price, delayed payment systems, and measurement limitations caused by privacy. In these circumstances, the rule-based accounting methods have difficulty in reflecting the economic substance of advertising transactions, and result in uncertainty in estimations, long delays in reconciliation, and increased audit risk. Artificial intelligence can be used as a viable avenue to overcome these challenges as it can be used to inference the considerations of the variable at data point based on the goals of dynamic price allocation and allocating transaction prices, and to identify the recognition in time with the duties of contractual performance. This paper introduces an artificial intelligence-based revenue recognition model of heterogeneous advertising platforms, which incorporates attribution modelling, uncertainty-sensitive prediction, and control-focused governance. The experiment findings show that model calibration, stability checks, and platform specific analysis are necessary to deploy to finance-grade. The results show that AI-based recognition of revenue, in conjunction with good governance and interpretability procedures, can increase accuracy, timeliness, and transparency in financial reporting in intricate advertising systems.
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