AITB No. 01 Accounting for AI-Driven Predictive Analytics in Financial Forecasting
Issue: How should entities account for the costs associated with developing and maintaining AI-driven predictive analytics tools used for financial forecasting?
Background: With the rise of AI in financial operations, many entities have started employing AI-driven predictive analytics to enhance their financial forecasting. These tools, while offering better accuracy, involve significant costs in terms of development, training, and maintenance.
Guidance:
- Capitalization of Development Costs: If the AI tool is expected to provide future economic benefits, the costs associated with its development should be capitalized as an intangible asset.
- Expensing of Training Data Costs: Costs associated with acquiring and curating data for training the AI should be expensed as incurred unless they directly contribute to a specific capitalized AI project.
- Amortization of AI Tools: The capitalized costs of the AI tool should be amortized over its useful life, considering both the technological lifespan and the period of expected economic benefits.
- Impairment Considerations: Given the rapidly changing nature of AI technologies, entities should regularly assess these assets for impairment.
Examples:
- Company A spends $1M developing an AI tool for financial forecasting and $200K on acquiring data to train the tool. They would capitalize the $1M and expense the $200K, then amortize the $1M over the tool's useful life.
Note: This is a fictional representation and does not represent any real-world standard for AI. The development of such standards would involve extensive consultations with experts, stakeholders, and the public. Fictional representations simplify complex AI concepts, stimulate discussion, envision future scenarios, highlight ethical considerations, encourage creativity, bridge knowledge gaps, and set benchmarks for debate in fields like accounting.