The advancements in artificial intelligence and machine learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are becoming more complex within the field of finance. Artificial intelligence and machine learning, with their spectacular success accompanied by unprecedented accuracies, have become increasingly important in the finance world.

Advanced Machine Learning Algorithms for Complex Financial Applications provides innovative research on the roles of artificial intelligence and machine learning algorithms in financial sectors with special reference to complex financial applications such as financial risk management in big data environments. In addition, the book addresses broad challenges in both theoretical and application aspects of artificial intelligence in the field of finance. Covering essential topics such as secure transactions, financial monitoring, and data modeling, this reference work is crucial for financial specialists, researchers, academicians, scholars, practitioners, instructors, and students.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Artificial Intelligence
Blockchain
Cryptocurrency
Data Management
Data Modeling
Digital Financial Landscapes
Financial Monitoring
Machine Learning Techniques
Risk Management
Secure Transactions
Supply Chain
Sustainable Development

Reviews & Statements

Finance is vital to the applications of machine learning. Several complex problems, such as investment decision making, macroeconomic analysis, asset credit evaluation, etc., widely exist in the field of finance. Machine learning (ML) is used in many financial companies which is making significant impact in the financial services. With the increasing complexity of financial transaction processes, ML can reduce operational costs through process automation which can automate repetitive tasks and increase productivity. The resurgence of AI/ML is so pervasive that it could virtually solve any problem from any field-be it theoretical or empirical with astounding results. It came very close to human intelligence and cognition, and in some cases even surpassed human experts. Banking, finance and insurance (BFSI) sector is no exception to this modern tsunami. Here, these technologies include traditional ML based predictive analytics, computational intelligence, deep learning, and reinforcement learning and deep reinforcement learning as well. The advancement in fin-tech especially artificial intelligence (AI) and machine learning (ML), has significantly affected the way financial services are offered and adopted today. ML is used in many financial companies which is making significant impact in the financial services. With the increasing complexity of financial transaction processes, ML can reduce operational costs through process automation which can automate repetitive tasks and increase productivity. The main objective of this book is to provide an exhaustive overview on the roles of AI and ML algorithms in financial sectors with special reference to complex financial applications such as financial risk management in big data environment.

– Mohammad Irfan, Associate Professor, CMR Institute of Technology, Bangalore, India

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Editor/Author Biographies

Mohammad Irfan is presently working as an Associate Professor at CMR Institute of Technology, Bangalore. Prior joining to CMRIT, he was associated with the School of Business, AURO University, Surat, Gujarat for five years. He is MBA (Finance and Marketing) and M.Com (Account and Law). Dr. Irfan has done this Ph.D. from the Central University of Haryana. He has qualified UGC-SRF/NET in Management and UGC-NET in Commerce. Dr. Irfan has also qualified NSEs Certification of Financial Market in Capital Market Model and BSEs Certification of Islamic Finance, Banking and Capital Market. He is working on seed money project, minor project and major project on social empowering issues. He has to his credit various research papers published in Scopus indexed journals including International of Business International Journal of Business Excellence (IJBEX), Montenegrin Journal of Economics-(ELIT), International Journal of Economics and Management (IJEM), Indian Journal of Finance (IJF). He is an editorial board member/reviewer in several national and international journals. Dr. Irfan has been teaching Graduate and Postgraduate Subjects for the last thirteen years in the areas of Security Analysis and Portfolio Management, Financial Analysis and Decision, Artificial Intelligence, Machine Learning, Block chain, Cryptocurrency, Data Analysis for Business, Financial Engineering, Financial Analytics, Financial modeling in Excel, Green Finance, and Alternative Finance. Dr. Irfan presented papers in the National and International conferences organized by leading institutions like IIM-A, IIM-C, IIM-Indore, IIM-Shillong, IIT-Roorkee, Indonesia, Malaysia, Switzerland, Nigeria sponsored by Islamic development bank (IDB-Saudi Arabia), Bank of Indonesia (BI), Malaysian Finance Association Conference (MFAC), Walisongo University (UIN Walisongo) Indonesia, and many more.

Salina Kassim has a great passion in writing scholarly articles in various areas of Islamic banking and finance. She has published extensively in academic journals with nearly 200 scholarly articles in the areas of her research interests. She has also published several books mainly in the areas of Islamic finance. In recognition to her dynamic role as a subject matter expert, she has been appointed as member of the editorial boards of several reputable international and local journals. At present, she is supervising (and has supervised) nearly 80 post-graduate candidates at the PhD and Masters levels. She has also served as internal and external examiners for Masters and PhD theses in several universities, apart from being appointed as Adjunct Professor, Visiting Research Fellow and trainer at several universities and institutes, locally and abroad.

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