Differences in IFRS and US GAAP Frameworks

The FASB establishes the Generally Accepted Accounting Standards in the United States (US GAAP), while the IASB establishes International Financial Reporting Standards (IFRS) outside the US.

Both the standards are working towards convergence and have a common worldwide standard. However, it's a slow process and at present there are many differences between the two frameworks:

Listed below are the important differences between the two frameworks:

  1. The U.S. GAAP provides separate objectives for business entities and nonentities while the IFRS provides one objective for both.
  2. Going Concern and Accrual concepts are not well developed in US GAAP. In IFRS, they form the underlying assumptions.
  3. IASB framework includes two elements related to financial performance, namely, income and expenses. FASB framework includes five elements: revenues, expenses, gains, losses, and comprehensive income. Comprehensive income has much broader scope than net income, as it also includes changes in equity.
  4. In FASB as asset is defined as a future economic benefit, while in IASB, an asset is a resource from which future expected benefits are expected to flow. Another important point is that FASB uses the word "probable" in its definition of assets and liabilities. IASB uses the word "probable" in its recognition criteria, however, with a different meaning.
  5. FASB does not allow revaluations, except for some financial instruments that need to be carried out at fair value.

While there are many differences in the two standards, these are the broad level differences at the conceptual level.

It is important for financial analysts to understand these differences while interpreting and comparing financial statements that are prepared under different accounting standards. Some companies also prepare reconciliation statements to make their financial results useful to a larger user group. For example, if a company uses US GAAP, it will also provide a reconciliation statement which will present the figures had the company used IFRS.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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