The Financial Application of Machine Learning Using R Software - read and apply key R methods for finance

Aug 2, 2026

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The financial sector increasingly adopts machine learning models in banking and insurance, and advanced analytics teams implement these methods in practice. The paper examines limitations of machine learning approaches and provides guidance for selecting suitable methods in financial applications, with emphasis on implementable workflows using R libraries. Core learning paradigms are introduced, followed by a focus on representative techniques and their usage for predictive and classification tasks in finance.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Supervised learning

Unsupervised learning

Literature Review

Methodology

Machine Learning Techniques

Linear Discriminant Analysis (LDA)

Logistic Regression (LR)

Decision Trees (DT)

Support Vector Machine (SVM)

The Financial Application of Machine Learning Using R Software Sami Mestiri University of Monastir, Tunisia Corresponding Author: Sami Mestiri [mestirisami2007@gmail.com](mestirisami2007@gmail.com) | A R T I C L E I N F O Keywords: Financial Application, Machine Learning, R Software | | A B S T R A C T
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 | | --- | --- | --- | | Received
Revised Accepted | : 3 June
: 18 June: 20July | | | ©2023 Mestiri: This is an
open-access article
distributed under the terms of the Creative Commons
| | | 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 entropybased, 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. LITERATURE REVIEW 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) ). 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. METHODOLOGY Machine Learning Techniques Linear Discriminant Analysis (LDA) Ronald Fisher (1933) 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 combination of descriptors is called discriminant function. The output of ADL is a score that is consists of classify a data observation between the good and bad classes. Where are the w

FAQs

Why is machine learning increasingly used in banking and insurance?

Because it supports predictive modeling by learning patterns from data without requiring explicit programming, which helps analytics teams build practical solutions regularly.

What guidance does the paper provide for applying machine learning in finance?

It analyzes limitations of machine learning methods and offers suggestions for choosing appropriate approaches for financial applications, supported by R library implementations.

Which machine learning techniques are covered in the methodology section?

The methodology presents techniques including Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machine (SVM), along with their modeling rationale.

Aug 2, 2026

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