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SwissCognitiveProperly deploying machine learning within an organization involves considering and answering three core questions:

The use of machine learning technology is spreading across all areas of modern organizations, and its predictive capabilities suit the finance function’s forward-looking needs. Understanding how to work with machine learning models is crucial for making informed investment decisions.

Yet, for many finance professionals, successfully employing them is the equivalent of navigating the Bermuda Triangle.

Properly deploying machine learning within an organization involves considering and answering three core questions:

  1. Does this project match the characteristics of a typical machine learning problem?
  2. Is there a solid foundation of data and experienced analysts?
  3. Is there a tangible payoff?

Does This Project Match the Characteristics of a Typical ML Problem?

Machine learning is a subset of artificial intelligence that’s focused on training computers to use algorithms for making predictions or classifications based on observed data.

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Finance functions typically use “supervised” machine learning, where an analyst provides data that includes the outcomes and asks the machine to make a prediction or classification based on similar data.

With “unsupervised” machine learning, data is provided without outcomes and the machine attempts to glean them. However, given the popularity of the supervised models within finance functions, our articles will focus on such models.

To present a very simple example in which you were attempting to train a model that predicts A + B = C using supervised machine learning, you would give it a set of observations of A, B, and the outcome C.

You would then tell an algorithm to predict or classify C, given A and B. With enough observations, the algorithm will eventually become very good at predicting C. With respect to this example, the problem is well solved by humans.

But what if the question was A+B+…+F(X) = Z?

Traditionally, humans would tackle that problem by simplifying the equation — by removing factors and introducing their own subjectivity. As a result, potentially important factors and data are not considered. A machine can consider all the factors and train various algorithms to predict Z and test its results.

In short, machine learning problems typically involve predicting previously observed outcomes using past data. The technology is best suited to solve problems that require unbiased analysis of numerous quantified factors in order to generate an outcome.

Is There a Solid Foundation of Data?

Machine learning models require data. As noted earlier, the data must also include observable outcomes, or “the right answer,” for machine learning to predict or classify.

For instance, if you are trying to predict what credit rating a private company might attain based on its financial statements, you need data that contains other companies’ financial statements and credit ratings. The ML model will look at all the financial statement data and the observable outcomes (in this case the other companies’ credit ratings), and then predict what the private company credit rating might be. […]


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