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New technology and changes in the regulatory framework have had a significant impact; various new players have emerged, and new business models have evolved. API-based ecosystems have become the new normal and collaboration in the financial and banking industry has reached new levels. Digital Project Practice for Banking and FinTech focuses on technology changes in the financial industry and their implications for business practice. A combination of practical experience in the field as well as academic research, the book explores a wide range of topics in the multifaceted landscape of FinTech. It examines the industry's various dimensions, implications, and potential based on academic research and practice. From project management in the digital era to the regulation and supervision of FinTech companies, the book delves into distinct aspects of this dynamic field, offering valuable insights and practical knowledge. It provides an in-depth overview of various unfolding developments and how to deal with and benefit from them. The book begins by exploring the unique challenges and opportunities project management presents in the digital era. It examines the evolving role of project management and provides strategies for effectively navigating the complexities of digital transformation initiatives. The book then covers such topics as: Financial Technology Canvas, a powerful tool for facilitating effective communication within fintech teams Process automation implementation in the financial sector and related benefits, challenges, and best practices to drive operational efficiency and enhance customer experiences Robotic process automation in financial institutions Cyptoeconomics and its potential implications for the diffusion of payment technologies The efficiency and risk factors associated with digital disruption in the banking sector. At its core, this book is about real-world practice in the digital banking industry. It is a source of different perspectives and diverse experiences from the global financial and banking industry.

Spolu s nastupujícími trendy v oblastech IT, digitalizace a cloud bankingu prochází globální bankovnictví v posledních letech dynamickým vývojem. Predkládaná publikace se snaží zájemce uvést do široké problematiky bankovnictví v teoretické i praktické rovine, a to s durazem na Ceskou republiku a Evropskou unii. Jedná se o unikátní bilingvní cesko-anglické dílo vycházející z nekolikaletého výzkumu autoru v oblasti financních trhu a risk managementu. Publikace je psána srozumitelným jazykem, a tudíž je vhodná jak pro odbornou, tak i pro širší verejnost

In 25 concise steps, you will learn the basics of blockchain technology.No mathematical formulas, program code, or computer science jargon are used.No previous knowledge in computer science, mathematics, programming, or cryptography is required.Terminology is explained through pictures, analogies, and metaphors.

It is a remarkable fact that images are related to objects constituting them. In this paper, we propose to represent images by using objects appearing in them. We introduce the novel concept of object bank (OB), a high-level image representation encoding object appearance and spatial location information in images. OB represents an image based on its response to a large number of pre-trained object detectors, or ‘object filters’, blind to the testing dataset and visual recognition task. Our OB representation demonstrates promising potential in high level image recognition tasks. It significantly outperforms traditional low level image representations in image classification on various benchmark image datasets by using simple, off-the-shelf classification algorithms such as linear SVM and logistic regression. In this paper, we analyze OB in detail, explaining our design choice of OB for achieving its best potential on different types of datasets. We demonstrate that object bank is a high level representation, from which we can easily discover semantic information of unknown images. We provide guidelines for effectively applying OB to high level image recognition tasks where it could be easily compressed for efficient computation in practice and is very robust to various classifiers.

This study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country.

PurposeThe purpose of this study is to propose a method of measuring service quality as well as suggesting to detect customer complaints through analysis of customer online reviews of mobile bank, which is unstructured data.Design/methodology/approachThis study uses text mining approach for customer online reviews analysis. The research procedure includes: (1) extracting users' reviews for Kakao Mobile Bank, (2) pre-processing of the extracted review data, (3) analyzing the sentiment of each review, (4) measuring the service quality score of each dimension by analyzing keyword frequency and network for each polarity, (5) evaluating total score for mobile bank service quality, and (6) detecting customer complaints on online reviews.FindingsThere are some findings. First, from the customer's point of view, it was possible to see which factors are important among the various dimensions of service quality and which factors should be managed well in mobile banking setting. Second, by periodically finding customer complaints, service failures can be prevented early, and service quality and customer satisfaction can be improved.Practical implicationsFrom a practical point of view, mobile bank managers should pay more attention to the service quality dimensions of practicality and enjoyment. In addition, the results mean that the app design and aesthetics with the most negative reviews should be reviewed from the user's perspective rather than from the company's point of view. Second, it is possible for them to establish a systematic complaint management system that can prevent service failure in advance by detecting customer complaints early. Third, it is possible for them to make quick decisions regarding service quality with the help of real-time customer response through dashboard construction.Originality/valueThis paper is a pioneer study measuring service quality with sentiment analysis, one of the text mining applications, using customers' reviews of a mobile bank.