What is a neural network in accounting and finance?
A neural network is a type of artificial intelligence that acts similarly to how humans process information. A neural network uses nodes that recognize inputs and process data to derive a desired output. What makes a neural network unique is that it can respond to changes in the data inputted and automatically adjust to still achieve the desired output. Typical statistical models rely on input data to be in a consistent format. So again, similar to how humans can process different information and react appropriately, that is what a neural network aims to accomplish.
What are the use cases for neural networks in accounting and finance?
Statistical models have been used for decades in accounting and finance to predict future outcomes or performance. However, those models always required adjustment if the inputted data changed. With neural networks, companies can use the data collected to plug into the neural network and not have to rework the entire model just because the data changed. Some example use cases for neural networks include:
Pattern recognition – Neural networks help finance and accounting professional identify patterns in the data they have collected, which allows the professional to make more informed business decisions. For example, sales data by customer could be processed through a neural network and could be used to identify customer purchasing behavior.
Forecasting data – Forecasting is critical for any company. A neural network brings another level of sophistication to forecasting.
Credit worthiness – Neural networks have been used to forecast bankruptcy, which helps banks assess a company’s credit worthiness when applying for a loan.
Valuation – Valuation models for every industry can be more accurate and sophisticated with the use of neural networks.
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