The possibilities and benefits of Machine Learning (ML) and Artificial Intelligence (AI) are very promising. The application of these techniques triggers enthusiasm all round. However, what do the terms ML and AI actually mean? In many cases, the terms AI and ML are mixed up and it seems confusing for many. So, let’s start by having a quick look at the definitions:
AI is the concept of machines being able to perform tasks usually performed by humans;
ML is an application of AI based on the idea that machines should be able to learn from data and improve themselves by making use of statistics;
Cool, so how does ML work?
ML is about finding and developing algorithms to train software-models. The models are used to predict an output for a specific input. Using the algorithms, the models are trained and the output validated. The input dataset will be split into two different datasets: one to train the models and the second to validate the output. If the output is correct, the model can be used for other datasets and thus used in a business setting. The learning and validation is a continues process.
There are a lot of statistics behind the models and the generated outcomes. The selection and learning of a model is an art in itself. The application of ML on business problems therefore is time-consuming and challenging. It involves using experts like data-scientists, which are scarce in current labour market.
Automated machine learning solve these issues. By automating building and using ML-models, the application of ML is now in reach for many. The automated algorithm runs automatically through the data and selects the models that generate the most relevant information. These automated algorithms make use of a variety of models to make ML more efficient in the organization, without the need for much statistical knowledge. So, ML is now available for your organization as well!
Example of a ML application
How ML could be used in practice is best shown by an example: an investment analysis:
Organizations often make the decision to invest based on the profitability of an investment (let’s forget other strategic reasons for now). This can be calculated based on future cashflows for the investment. However, how can future incomes be predicted when no historic data is available?
To solve this problem, data can be generated that is representative of expected income and costs of the investment. For this goal, Monte Carlo simulations can be applied. Using this technique, the value of an investment can be predicted and the decision can be made whether an investment is financially responsible. The statistical techniques help by generating representative data and calculate a prognosis. This helps to improve the decision-making process.
How can we help?
CPMview provides tooling, which can help organizations to apply ML in financial reporting and analysis of daily operations. This makes decision-making more efficient and quantitively-based. Which is done by implementing automated machine learning tools that run in the cloud, or on the background of your desktop while you can work on your daily tasks.
In the next blog, we will dive deeper into: ‘How Machine learning can be used within the Finance Function’.