Understanding the types of algorithms in and what they accomplish can help CFOs ask the right questions when working with data. Applying skepticism is a healthy way to evaluate models and their outcomes. Both approaches will benefit financial professionals as they provide context to employees who are engaging in their organizations.
Chief financial officers today face more opportunities to engage with within the corporate finance function of their organizations. As they encounter these projects, they’ll work with employees and vendors and will need to communicate effectively to get the results they want.
The good news is that finance executives can have a working understanding of algorithms, even if they don’t have a computer science background. As more organizations turn to to predict key business metrics and solve problems, learning how algorithms are applied and how to assess them will help financial professionals glean information to lead their organization’s financial activity more effectively.
Machine learning is not a single methodology but rather an overarching term that covers a number of methodologies known as algorithms.
Enterprises use to classify data, predict future outcomes, and gain other insights. Predicting sales at new retail locations or determining which consumers will most likely buy certain products during an online shopping experience represent just two examples of .
A useful aspect about is that it is relatively easy to test a number of different algorithms simultaneously. However, this mass testing can create a situation where teams select an algorithm based on a limited number of quantitative criteria, namely accuracy and speed, without considering the methodology and implications of the algorithm. The following questions can help finance professionals better select the algorithm that best fits their unique task.
Four questions you should ask when assessing an algorithm:
1. Is this a classification or prediction problem? There are two main types of algorithms: classification and prediction. The first form of data analysis can be used to construct models that describe classes of data using labels. In the case of a financial institution, a model can be used to classify what loans are most risky and which are safer. Prediction models on the other hand, produce numerical outcome predictions based on data inputs. In the case of a retail store, such a model may attempt to predict how much a customer will spend during a typical sales event at the company.
Financial professionals can comprehend the value of classification by seeing how it handles a desired task. For example, classification of accounts receivables is one way algorithms can help CFOs make decisions. Suppose a company’s usual accounts receivable cycle is 35 days, but that figure is simply an average of all payment terms. Machine learning algorithms provide more insight to help find relationships in the data without introducing human bias. That way, financial professionals can classify which invoices need to be paid in 30, 45, or 60 days. Applying the correct algorithms in the model can have a real business impact.[…]
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