Financial applications of machine learning using R software - Suggestions on method choice

Aug 2, 2026

Financial applications increasingly rely on machine learning models in banking and insurance, with analytics teams implementing these approaches in practice. This paper examines the limitations of machine learning methods for financial contexts and then provides guidance on selecting suitable methods for finance-related applications. It discusses key learning paradigms and introduces how R libraries can be used to compute machine learning methods relevant to financial research, supporting more effective model adoption.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Supervised learning

Unsupervised learning

Machine learning techniques

Linear Discriminant Analysis (LDA)

Logistic Regression (LR)

Decision Trees (DT)

Munich Personal RePEc Archive Financial applications of machine learning using R software Mestiri, Sami université de Monastir 2024 Online at [https://mpra. ub. uni-muenchen. de/119998/](https://mpra. ub. uni-muenchen. de/119998/) [MPRA Paper No. 119998](MPRA Paper No. 119998) , [posted 13 Feb 2024 08:08 UTC](posted 13 Feb 2024 08:08 UTC) Financial applications of machine learning using R software Sami Mestiri 1 Applied Economics and Simulation Faculty of Management and Economic Sciences of Mahdia, University of Monastir, Tunisia. Rue Ibn Sina Hiboun, Mahdia Tunisia Abstract: In the last years, the ﬁnancial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the ﬁnancial community are implementing these models regularly. In this paper, we analyses the limitations of machine learning methods, and then provides some suggestions on the choice of methods in ﬁnancial applications. We refer the reader to the R libraries that can be used to compute the Machine learning methods JEL codes: C45, G00 Keywords : Financial Application; Machine learning ; R software. 1 Introduction Machine learning (ML) is an application of Artiﬁcial Intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed. In eﬀect, it is about developing predictive models that can access data and use it to learn on their own. There are several types of learning, we distinguish: Supervised learning: is done using a truth, that is, we have prior knowledge of what the output values for our samples should be. Therefore, the goal of this type of learning is to learn a function that given a sample of data and the desired results, in order to best approximate the relationship between observable inputs and outputs. There are two types of supervised learning. Classiﬁcation algorithms which seek to predict a class/category and Regression algorithms which seek to predict a continuous value. Unsupervised learning: aims to data structure inference. The two most common subcategories in unsupervised learning are clustering and dimensionality reduction. In clustering observations are grouped in such a method as to produce high intra-group similarity and low inter-group similarity. The diﬀerent types of clustering methods that have been proposed are entropy-based, density-based and distribution-based methods. Reduction of dimensionality aims to increase the information density of the data by reducing their dimensionality while retaining most of the inherent information. There are diﬀerent techniques based on principal component analysis (PCA) which derive linear combinations of the original variables to cover as much of the variance in the data as possible. Second, neural network-based methods reduce dimensionality with particular architectures. 1 Email : mestirisami2007@gmail:com AI is increasingly entering our daily lives with impressive applications. This article discusses the use of ML to solve problems in ﬁnance research. The contribution of this article is threefold. First, we provide an introduction to Machine Learning. Next we pay particular attention to the diﬀerent R package implemented (see Mestiri.S (2019) [23]) . We build a taxonomy of current and future ML applications in ﬁnance. Finally, we study the prospects of ML applications in ﬁnance. The research paper is organized as follows: Section 2 presents the diﬀerent Machine Learning techniques used. In section 3, we present a taxonomy of existing ML applications. The ﬁfth section is devoted to limitation and perspective. Finally, we conclude in section 6 . 2 Machine learning techniques 2.1 Linear Discriminant Analysis (LDA) Ronald Fisher (1933)[10] pioneered work on discriminant analysis. In his work, he developed a statistical technique for defaults prediction, by developing a linear combination of quantitative predictor variables. This linear combin

FAQs

What problem does machine learning address in finance according to this paper?

It describes predictive modeling that learns from data without explicit programming, and focuses on how such models can be used to solve finance research problems.

How does the paper distinguish between supervised and unsupervised learning?

Supervised learning learns functions using known output values, with classification and regression variants. Unsupervised learning infers data structure, commonly via clustering or dimensionality reduction.

Which R-based methods are highlighted for machine learning in this document?

The paper discusses Linear Discriminant Analysis using the MASS library (lda), Logistic Regression using the stats library (glm with binomial family), and Decision Trees using splitting criteria such as Gini impurity.

Aug 2, 2026

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