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Novel Financial Applications of Machine Learning and Deep Learning

Part of the International Series in Operations Research & Management Science Book Series (volume 336)

Editors: Abedin, Mohammad Zoynul; Hajek, Petr

DOI: 10.1007/978-3-031-18552-6

ISBN: 978-3-031-18551-9

Publisher: Springer International Publishing - Springer Journals

Mar 2, 2023

1 - 10 of 13 Chapters

LitStream

FinTech Risk Management and Monitoring

Hasan, Morshadul; Hoque, Ariful

Pages: 3 –16

[The recent emergence of financial technology innovations in the financial services industry also faces many challenges due to some significant risks. This chapter aims to identify specific fintech risks and appropriate ways to manage the risks. A qualitative research method is used to explore...
LitStream

Digital Transformation of Supply Chain with Supportive Culture in Blockchain Environment

Akter, Shakila; Haque, Mohammad Samiul; Sohag, Ashrafuzzaman; Siddikee, Md. Jahangir Alam; Abedin, Mohammad Zoynul

Pages: 17 –33

[This study aims to evaluate the effect and usefulness of digital transformation of supply chain management (SCM) on blockchain with a supportive culture. This paper explores the effect of blockchain on SCM under consideration of automated controls with smart contracts, fundamental attributes,...
LitStream

Integration of Artificial Intelligence Technology in Management Accounting Information System: An Empirical Study

Chowdhury, Emon Kalyan

Pages: 35 –46

[At present, most of the business organizations take their management decisions using traditional approach. In the traditional approach, the freedom to be flexible is limited due to numerous assumptions. This paper aims to establish an artificial neural network-based model to predict management...
LitStream

The Impact of Big Data on Accounting Practices: Empirical Evidence from Africa

Bonsu, Mandella Osei-Assibey; Roni, Naheed; Guo, Yongsheng

Pages: 47 –71

[Big data is much more than accounting and financial data. Big data including financial and non-accounting data have become accessible in immense volumes in distinct forms and in real time. The use of big data for accounting is immobile in initial periods. However, academics have predicted that...
LitStream

Using Outlier Modification Rule for Improvement of the Performance of Classification Algorithms in the Case of Financial Data

Auwul, Md. Rabiul; Hakim, Md. Ajijul; Dhonno, Fahmida Tasnim; Shilpa, Nusrat Afrin; Sohag, Ashrafuzzaman; Abedin, Mohammad Zoynul

Pages: 75 –92

[This study aims to improve the performance of Data Analytics (DA) algorithms by mining outliers from credit card fraud detection datasets. In doing so, we analyze the performance of data analytics algorithms, such as Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Naïve Bayes (NB)...
LitStream

Default Risk Prediction Based on Support Vector Machine and Logit Support Vector Machine

Fahmida-E-Moula,; Shilpa, Nusrat Afrin; Shaha, Preity; Hajek, Petr; Abedin, Mohammad Zoynul

Pages: 93 –106

[This chapter aims to predict the credit customer default risk. We propose a machine learning algorithm such as Support Vector Machine and a hybrid default risk prediction model such as Logistic Regression and Support Vector Machine being known as LogitSVM (LSVM) to access the credit default...
LitStream

Predicting Corporate Failure Using Ensemble Extreme Learning Machine

Veganzones, David

Pages: 107 –124

[Corporate failure prediction has become a major topic in the accounting and finance literature. Effective prediction models are essential for banks and financial institutions to solve financial decision-making problems. In general, artificial intelligence and machine learning techniques have...
LitStream

Assessing and Predicting Small Enterprises’ Credit Ratings: A Multicriteria Approach

Shi, Baofeng

Pages: 125 –149

[Credit ratings play a key role in helping financial institutions to make loan decisions and to reduce the financial constraints on small and medium-sized enterprises. However, small enterprises have made it difficult for financial institutions such as commercial banks to accurately determine...
LitStream

An Ensemble LGBM (Light Gradient Boosting Machine) Approach for Crude Oil Price Prediction

Sajid, Sad Wadi; Hasan, Mahmudul; Rabbi, Md. Fazle; Abedin, Mohammad Zoynul

Pages: 153 –165

[Crude oil is considered one of the most important resources in the world today. Most of the fuel used today is refined from crude oil. Fuel also has a great impact on the global economy. The crude oil market is liquid and uncertain. The prediction of the crude oil market price has become a...
LitStream

Model Development for Predicting the Crude Oil Price: Comparative Evaluation of Ensemble and Machine Learning Methods

Hasan, Mahmudul; Das, Ushna; Datta, Rony Kumar; Abedin, Mohammad Zoynul

Pages: 167 –179

[The crude oil market is unstable, and its price is highly volatile. Due to the Covid-19 pandemic, the price of crude oils goes up and down in a short period of time. Future plans and projects’ policies depend directly and indirectly on the future price of crude oil. So, the aim of this study is...

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