GPT Classifications, with Application to Credit Lending

12 Pages Posted: 6 Dec 2023 Last revised: 16 Jun 2024

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Abstract

Generative Pre-trained Transformers (GPT) and Large language models (LLMs) have made significant advancements in natural language processing in recent years. The practical applications of LLMs are undeniable, rendering moot any debate about their impending influence. The power of LLMs has made them similar to machine learning models for decision-making problems. In this paper, we focus on binary classification which is a common use of ML models, particularly in credit lending applications. We show how a GPT model can perform almost as accurately as a classical logistic machine learning model but with a much lower number of sample observations. In particular, we show how, in the context of credit lending, LLMs can be improved and reach performances similar to classical logistic regression models using only a small set of examples.

Keywords: Artificial Intelligence, Large Language Models, Credit Decisions

Suggested Citation

Babaei, Golnoosh and Giudici, Paolo, GPT Classifications, with Application to Credit Lending. Available at SSRN: https://ssrn.com/abstract=4636796 or http://dx.doi.org/10.2139/ssrn.4636796

Golnoosh Babaei

University of Pavia ( email )

Via San Felice
5
Pavia, Pavia 27100
Italy

Paolo Giudici (Contact Author)

University of Pavia ( email )

Via San Felice 7
27100 Pavia, 27100
Italy

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