목차
1. Introduction
2. Literature Review
2.1 The Usage of AI-based technologies on financial accounting
2.2 The role of AI-based technologies in auditing procedure
2.3 The ethical dilemmas from accounting firms
2.4 Efforts to deal with the ethical dillemas
3. Conclusion
2. Literature Review
2.1 The Usage of AI-based technologies on financial accounting
2.2 The role of AI-based technologies in auditing procedure
2.3 The ethical dilemmas from accounting firms
2.4 Efforts to deal with the ethical dillemas
3. Conclusion
본문내용
Using AI technology can help accounting experts and finance analysts find out what transactions happened during the accounting period. They can also use it to make financial statements like the income statement, balance sheet, and cash flow statement using adjusted trial balances.
Mokhtari et al. (2019) explained that LSTM stands for Long Short-Term Memory, which is a type of artificial neural network architecture used in deep learning. In other words, deep learning is applied to help financial analysts generate more accurate annotations by applying advanced AI technologies to financial reports than annotating financial reports using existing neural networks.
When preparing financial statements for internal purposes, AI will facilitate organizations to provide more timely and accurate financial statements. This is due to the nature and ability of AI to analyze and interpret data on a much faster scale than humans.
Petkov (2020) maintained that AI is created in a way that has upper and lower limits for machines to exercise their judgment. By empowering AI to enter accounting functions, human error in handling them can be minimized. For example, scan your bank account and delegate it to AI to identify the transaction. AI automatically enters adjustments at a timely time and delegates LCM estimates to AI by providing a standard creation tool sheet that captures the market value of inventory from third parties
Mokhtari et al. (2019) explained that LSTM stands for Long Short-Term Memory, which is a type of artificial neural network architecture used in deep learning. In other words, deep learning is applied to help financial analysts generate more accurate annotations by applying advanced AI technologies to financial reports than annotating financial reports using existing neural networks.
When preparing financial statements for internal purposes, AI will facilitate organizations to provide more timely and accurate financial statements. This is due to the nature and ability of AI to analyze and interpret data on a much faster scale than humans.
Petkov (2020) maintained that AI is created in a way that has upper and lower limits for machines to exercise their judgment. By empowering AI to enter accounting functions, human error in handling them can be minimized. For example, scan your bank account and delegate it to AI to identify the transaction. AI automatically enters adjustments at a timely time and delegates LCM estimates to AI by providing a standard creation tool sheet that captures the market value of inventory from third parties
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