Novel Financial Applications of Machine Learning and Deep Learning [electronic resource] : Algorithms, Product Modeling, and Applications / edited by Mohammad Zoynul Abedin, Petr Hajek.

Part 1: Recent Developments in FinTech -- 1. FinTech Risk Management and Monitoring -- 2. Digital Transformation of Supply Chain with Supportive Culture in Blockchain Environment -- 3. Integration of Artificial Intelligence Technology in Management Accounting Information System - An Empirical Study -- 4. The Impact of Big Data on Accounting Practices: Empirical Evidence from Africa -- Part 2: Financial Risk Prediction using Machine Learning -- 5. Using Outlier Modification Rule for Improvement of the Performance of Classification Algorithms in the Case of Financial Data -- 6. Default Risk Prediction Based on Support Vector Machine and Logit Support Vector Machine -- 7. Predicting Corporate Failure using Ensemble Extreme Learning Machine -- 8. Assessing and Predicting Small Enterprises' Credit Ratings: A Multicriteria Approach -- Part 3: Financial Time-Series Forecasting -- 9. An Ensemble LGBM (Light Gradient Boosting Machine) Approach for Crude Oil Price Prediction -- 10. Model Development for Predicting the Crude Oil Price: Comparative Evaluation of Ensemble and Machine Learning Methods -- part 4: Emerging Technologies in Financial Education and Healthcare -- 11. Discovering the Role of M-learning among Finance Students: The Future of Online Education -- 12. Exploring the Role of Mobile Technologies in Higher Education: The Impact of Online Teaching on Traditional Learning.-13. Knowledge Mining from Health Data: Application of Feature Selection Approaches.

This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.