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How AI Models Are Built

In: Artificial Intelligence and Credit Risk

Author

Listed:
  • Rossella Locatelli

    (University of Insubria)

  • Giovanni Pepe

    (KPMG Advisory)

  • Fabio Salis

    (Credito Valtellinese)

Abstract

This chapter describes the various kinds of data that are mostly in use today in AI models, differentiating between “structured”, “semi-structured” and “unstructured” data. Text analysis and Natural Language Processing are illustrated as the main structuring techniques for unstructured data. Some examples of alternative credit data are described, including among others transactional data, data extracted from telephones and other utilities, data extracted from social profiles, data extracted from the world wide web and data gathered through surveys/questionnaires. Also, the chapter describes the opportunity of estimating a model only by means of machine learning techniques, detailing the characteristics of the most used ML algorithms: decision trees, random forests, gradient boosting and neural networks. The application of a special type of neural network is detailed: the autoencoder.

Suggested Citation

  • Rossella Locatelli & Giovanni Pepe & Fabio Salis, 2022. "How AI Models Are Built," Springer Books, in: Artificial Intelligence and Credit Risk, chapter 0, pages 9-27, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-10236-3_2
    DOI: 10.1007/978-3-031-10236-3_2
    as

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