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Predictably Unequal? The Effects of Machine Learning on Credit Markets

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Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Predictive modelling

Predictive modelling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. In many cases, the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.

Predictive analytics

Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.

Credit rating agency

A credit rating agency (CRA, also called a ratings service) is a company that assigns credit ratings, which rate a debtor's ability to pay back debt by making timely principal and interest payments and the likelihood of default. An agency may rate the creditworthiness of issuers of debt obligations, of debt instruments, and in some cases, of the servicers of the underlying debt, but not of individual consumers. Other forms of a rating agency include environmental, social and corporate governance (ESG) rating agencies and the Chinese Social Credit System.

Credit derivative

In finance, a credit derivative refers to any one of "various instruments and techniques designed to separate and then transfer the credit risk" or the risk of an event of default of a corporate or sovereign borrower, transferring it to an entity other than the lender or debtholder. An unfunded credit derivative is one where credit protection is bought and sold between bilateral counterparties without the protection seller having to put up money upfront or at any given time during the life of the deal unless an event of default occurs.

Structuralism

In sociology, anthropology, archaeology, history, philosophy, and linguistics, structuralism is a general theory of culture and methodology that implies that elements of human culture must be understood by way of their relationship to a broader system. It works to uncover the structures that underlie all the things that humans do, think, perceive, and feel. Alternatively, as summarized by philosopher Simon Blackburn, structuralism is:[T]he belief that phenomena of human life are not intelligible except through their interrelations.

Functional linguistics

Functional linguistics is an approach to the study of language characterized by taking systematically into account the speaker's and the hearer's side, and the communicative needs of the speaker and of the given language community. Linguistic functionalism spawned in the 1920s to 1930s from Ferdinand de Saussure's systematic structuralist approach to language (1916). Functionalism sees functionality of language and its elements to be the key to understanding linguistic processes and structures.

Credit default swap

A credit default swap (CDS) is a financial swap agreement that the seller of the CDS will compensate the buyer in the event of a debt default (by the debtor) or other credit event. That is, the seller of the CDS insures the buyer against some reference asset defaulting. The buyer of the CDS makes a series of payments (the CDS "fee" or "spread") to the seller and, in exchange, may expect to receive a payoff if the asset defaults.

Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.

Automated machine learning

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning.