Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

2022 ◽  
pp. 187-204
Author(s):  
María A. Pérez-Juárez ◽  
Javier M. Aguiar-Pérez ◽  
Miguel Alonso-Felipe ◽  
Javier Del-Pozo-Velázquez ◽  
Saúl Rozada-Raneros ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Financial Institutions ◽  
Financial Services ◽  
Essential Role ◽  
Digital Natives ◽  
Digital Immigrants ◽  
Artificial Intelligence Techniques ◽  
New Challenges

A lot of millennials have been educated in gamified schools where they played Kahoot several times per week, and where applications like Classcraft made them feel like the protagonists of a videogame in which they had to accumulate points to be able to level up. All those that were educated in a gamified environment feel it is natural and logical that gamification is used in all areas. For this reason, gamification is increasingly becoming important in different fields including financial services, bringing new challenges. Gamification allows financial institutions to provide personalized and compelling experiences. Big data and artificial intelligence techniques are called to play an essential role in the gamification of financial services. This chapter aims to explore the possibilities of using artificial intelligence and big data techniques to support gamified financial services which are essential for digital natives but also increasingly important for digital immigrants.

scholarly journals Cooperative Governance Path of Financial Technology Supervision

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Difei Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Financial Institutions ◽  
Financial Services ◽  
Big Data Analysis ◽  
Technology Changes ◽  
Financial Technology ◽  
Cooperative Governance

Financial technology changes the logic of financial interpretation through the use of digital and digital centric technologies, commercialization, big data analysis, machine learning and artificial intelligence. From financial institutions that use technology to provide financial services to technology companies that directly provide financial services, fintech companies play an important role in realizing financial brokerage and financial democratization and improving the availability and efficiency of financial services. Based on this, this paper focuses on the plight and path of cooperative governance of financial technology supervision, for the reference of relevant personnel.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

When Technology Meets Finance

2021 ◽  
pp. 1-21
Author(s):  
Yousif Abdullatif Albastaki
Keyword(s):  
Artificial Intelligence ◽  
Financial Institutions ◽  
Financial Services ◽  
Major Part ◽  
The Internet ◽  
Financial Industry ◽  
Digital Innovation ◽  
Customer Expectations ◽  
The Internet Of Things

There is a paradigm shift in the financial services industry. Combined with ever-changing customer expectations and preferences, emerging technologies such as artificial intelligence (AI), machine learning, the internet of things (IoT), and blockchain are redefining how financial institutions deliver services. It is an enormous task to remain competitive in this ever-changing environment. Financial institutions see FinTech as a major part of the digital future, and as proof of this, since 2015, financial institutions have invested over US$ 27 billion in FinTech and digital innovation. This chapter is an introductory chapter that explores FinTech in the literature. It focuses on how FinTech is reshaping the financial industry by describing FinTech phases and development process. The financial products and services using FinTech are also described with a highlight on Islamic FinTech. The chapter finally concludes by describing the future of FinTech.

Artificial Intelligence and SHGs

2020 ◽  
pp. 291-303
Author(s):  
Vinay Kandpal ◽  
Osamah Ibrahim Khalaf
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Big Data ◽  
Financial Services ◽  
Efficient Solution ◽  
Cost Effective ◽  
Financial Inclusion ◽  
Women Empowerment ◽  
Loan Repayment ◽  
Brick And Mortar

For inclusive growth and sustainable development of SHG and women empowerment, there is a need to provide an environment to access quality services from financial and non-financial agencies. While banks cannot reach all people through a ‘brick and mortar' model, new and advanced banking technology has enabled financial inclusion through branchless banking. By using artificial intelligence in banking, banks have a cost-effective and efficient solution to provide access to services to the financially excluded. Digital technology improves the accessibility and affordability of financial services for the previously unbanked or underbanked individuals and MSMEs. A big data-driven model can also be helpful for psychometric evaluations. Several psychometric tools help evaluate the applicant's answers which aid to capture information that can help to predict loan repayment behavior, comprising applicants' beliefs, performance, attitudes, and integrity.

GDPR vs. Big Data & AI in FinTechs

2020 ◽  
Vol 89 (4) ◽  
pp. 55-72
Author(s):  
Nermin Varmaz
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Protection ◽  
Financial Services ◽  
High Speed ◽  
Personal Data ◽  
General Data Protection Regulation ◽  
Spending Patterns ◽  
General Data ◽  
Initial Processing

Summary: This article addresses the compliance of the use of Big Data and Artificial Intelligence (AI) by FinTechs with European data protection principles. FinTechs are increasingly replacing traditional credit institutions and are becoming more important in the provision of financial services, especially by using AI and Big Data. The ability to analyze a large amount of different personal data at high speed can provide insights into customer spending patterns, enable a better understanding of customers, or help predict investments and market changes. However, once personal data is involved, a collision with all basic data protection principles stipulated in the European General Data Protection Regulation (GDPR) arises, mostly due to the fact that Big Data and AI meet their overall objectives by processing vast data that lies beyond their initial processing purposes. The author shows that within this ratio, pseudonymization can prove to be a privacy-compliant and thus preferable alternative for the use of AI and Big Data while still enabling FinTechs to identify customer needs. Zusammenfassung: Dieser Artikel befasst sich mit der Vereinbarkeit der Nutzung von Big Data und Künstlicher Intelligenz (KI) durch FinTechs mit den europäischen Datenschutzgrundsätzen. FinTechs ersetzen zunehmend traditionelle Kreditinstitute und gewinnen bei der Bereitstellung von Finanzdienstleistungen an Bedeutung, insbesondere durch die Nutzung von KI und Big Data. Die Fähigkeit, eine große Menge unterschiedlicher personenbezogener Daten in hoher Geschwindigkeit zu analysieren, kann Einblicke in das Ausgabeverhalten der Kunden geben, ein besseres Verständnis der Kunden ermöglichen oder helfen, Investitionen und Marktveränderungen vorherzusagen. Sobald jedoch personenbezogene Daten involviert sind, kommt es zu einer Kollision mit allen grundlegenden Datenschutzprinzipien, die in der europäischen Datenschutzgrundverordnung (DS-GVO) festgelegt sind, vor allem aufgrund der Tatsache, dass Big Data und KI ihre übergeordneten Ziele durch die Verarbeitung großer Datenmengen erreichen, die über ihre ursprünglichen Verarbeitungszwecke hinausgehen. Der Autor zeigt, dass sich in diesem Verhältnis die Pseudonymisierung als datenschutzkonforme und damit vorzugswürdige Alternative für den Einsatz von KI und Big Data erweisen kann, die FinTechs dennoch in die Lage versetzt, Kundenbedürfnisse zu erkennen.

scholarly journals Integration of Cloud Computing, Big Data, Artificial Intelligence, and Internet of Things

2021 ◽  
Vol 16 (1) ◽  
pp. 10-17
Author(s):  
Drissi Saadia
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Detailed Analysis ◽  
New Paradigm ◽  
Research Papers ◽  
Research Issues ◽  
New Challenges ◽  
Open Issues

Cloud computing, internet of things (IoT), artificial intelligence, and big data are four very different technologies that are already discussed separately. The use of the four technologies is required to be more and more necessary in the present day in order to make them important components in today's world technology. In this paper, the authors center their attention on the integration of cloud, IoT, big data, and artificial intelligence. Several kinds of research papers have surveyed artificial intelligence, cloud, IoT, and big data separately and, more precisely, their main properties, characteristics, underlying technologies, and open issues. However, to the greatest of the authors' knowledge, these works require a detailed analysis of the new paradigm that combines the four technologies, which suggests completely new challenges and research issues. To bridge this gap, this paper presents a survey on the integration of cloud, IoT, artificial intelligence, and big data.

scholarly journals Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

TEM Journal ◽  
2021 ◽  
pp. 1621-1629
Author(s):  
Aayat Aljarrah ◽  
Mustafa Ababneh ◽  
Damla Karagozlu ◽  
Fezile Ozdamli
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Distance Education ◽  
Big Data ◽  
Distance Learning ◽  
Affective Computing ◽  
Learning System ◽  
Educational Systems ◽  
Artificial Intelligence Techniques ◽  
Educational Processes

In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

scholarly journals Application of Artificial Intelligence in Investment Banks

2018 ◽  
Vol 11 (2) ◽  
pp. 131-136 ◽  
Author(s):  
R. Vedapradha ◽  
Hariharan Ravi
Keyword(s):  
Artificial Intelligence ◽  
Money Laundering ◽  
Financial Institutions ◽  
Financial Services ◽  
Investment Banks ◽  
Data Driven ◽  
Financial Services Industry ◽  
Smart Contracts ◽  
Customer Journey ◽  
Functional Areas

AbstractBanks are automating their processes, migrating their infrastructure and applications to the cloud to create a seamless customer journey. Transformative technology has enabled banks and financial institutions to automate their operations based on advanced data-driven. Banks are adopting AI based anti-money-laundering, anti-fraud, compliance, credit-underwriting and smart contracts technology in their operations. These applications have been embraced by the investment banks as regulatory framework are failing to combat conventional way in combating against money laundering. Artificial Intelligence will focus on cognitive application in functional areas of business along with investment and compliance sectors of financial services industry. Adopting AI based anti-money-laundering, anti-fraud, compliance, credit-underwriting and smart contracts technology in their operations.

scholarly journals Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

2021 ◽  
Vol 7 ◽  
pp. e488
Author(s):  
Amir Masoud Rahmani ◽  
Elham Azhir ◽  
Saqib Ali ◽  
Mokhtar Mohammadi ◽  
Omed Hassan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Optimization Theory ◽  
Artificial Intelligence Techniques ◽  
Knowledge Based ◽  
Complete Survey ◽  
The Internet Of Things

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

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