How AI Models Are Built

Rossella Locatelli (), Giovanni Pepe () and Fabio Salis ()
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Rossella Locatelli: University of Insubria
Giovanni Pepe: KPMG Advisory
Fabio Salis: Credito Valtellinese

Chapter Chapter 2 in Artificial Intelligence and Credit Risk, 2022, pp 9-27 from Springer

Abstract: 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.

Keywords: Structured data; Unstructured data; Text mining; NLP—Natural Language Processing; Topic modelling; Part-of-speech tagging; Sentiment analysis; Data cleansing; Dictionary; Alternative credit data; Transactional data; Machine Learning—ML; Artificial Intelligence—AI; Decision tree; Random forest; Gradient boosting; Neural network; Autoencoder (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10236-3_2

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DOI: 10.1007/978-3-031-10236-3_2

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