scholarly journals The Impact of the General Data Protection Regulation on the Financial Services’ Industry of Small European States*

2019 ◽  
Vol VII (Issue 4) ◽  
pp. 243-266 ◽  
Author(s):  
Kieran Xuereb ◽  
Simon Grima ◽  
Frank Bezzina ◽  
Andre Farrugia ◽  
Pierpaolo Marano
Keyword(s):  
Data Protection ◽  
Financial Services ◽  
Financial Services Industry ◽  
General Data Protection Regulation ◽  
General Data ◽  
Services Industry ◽  
The Impact

scholarly journals Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171986054 ◽  
Author(s):  
Heike Felzmann ◽  
Eduard Fosch Villaronga ◽  
Christoph Lutz ◽  
Aurelia Tamò-Larrieux
Keyword(s):  
Artificial Intelligence ◽  
Data Protection ◽  
Contextual Factors ◽  
Information Provision ◽  
Human Robot Interaction ◽  
Future Research ◽  
General Data Protection Regulation ◽  
Wide Range ◽  
General Data ◽  
The Impact

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.

Assessing the impact of the USA PATRIOT Act on the financial services industry

2005 ◽  
Vol 8 (3) ◽  
pp. 243-251 ◽  
Author(s):  
James Fisher ◽  
James Gilsinan ◽  
Ellen Harshman ◽  
Muhammed Islam ◽  
Fred Yeager
Keyword(s):  
Financial Services ◽  
Financial Services Industry ◽  
Patriot Act ◽  
Usa Patriot Act ◽  
The Usa ◽  
Services Industry ◽  
The Impact

The impact of entrepreneurial leadership on innovation management and its measurement validation

2017 ◽  
Vol 9 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Avanti Fontana ◽  
Soebowo Musa
Keyword(s):  
Financial Services ◽  
Innovation Management ◽  
Innovation Performance ◽  
Financial Services Industry ◽  
Entrepreneurial Leadership ◽  
Content Type ◽  
Two Samples ◽  
Measurement Validation ◽  
Services Industry ◽  
The Impact

Purpose This paper aims to validate the measurement of entrepreneurial leadership (EL) in the context of innovation management and strategic entrepreneurship, and to examine the relationship between EL and the innovation process (IP). It proposes the measurement of EL and outlines the reason and the importance of EL in the IP. The study further examines whether the IP would have direct impact on innovation performance. Design/methodology/approach The paper opted for an explanatory and confirmatory study using a quantitative approach employing an online survey/questionnaire distributed to two groups of employees representing middle and senior management having mixed background such as finance, marketing, operations and management. The first group consists of 222 respondents spread across multiple industries, and the second group consists of 60 respondents mainly from the financial services industry to validate the measurement of the EL construct. Findings The paper provides empirical insights into the validation of EL measurement through two samples, and on the impact of EL in fostering all elements in the IP (i.e. idea generation, idea selection and development or idea conversion and idea diffusion). The paper also confirms some of the literature views on the difficulty of identifying a significant relationship between the IP and innovation performance. It suggests counterintuitively that the IP may not necessarily have a positive relationship with innovation performance. Research limitations/implications Most of the respondents were those from the financial services industry, which may have an impact on the overall model but less on the validation of the EL measurement. The research affirms the theoretical concept of the dimensions of EL and validates its measurement. The research also shows intriguing findings on the missing link between the IP and innovation performance. Therefore, researchers are encouraged to identify variables or factors that should link the influence of the IP on innovation performance so that the contribution of innovation management to competitiveness can be clearly identified. Practical implications The research validates the measurement of the EL construct, which could be used as a screening tool in measuring the EL capacity at all levels within an organization as part of its leadership development in fostering its IP. Originality/value This paper fulfills an identified need to have a validated measurement of EL and its relationship with the IP.

Internet of Things and Its Impact on Financial Services

2021 ◽  
pp. 214-231
Author(s):  
Deepika Dhingra ◽  
Shruti Ashok
Keyword(s):  
Internet Of Things ◽  
Financial Services ◽  
High Tech ◽  
The Internet ◽  
Financial Services Industry ◽  
Daily Lives ◽  
Digital Devices ◽  
Services Industry ◽  
The Impact ◽  
The Internet Of Things

The internet of things (IoT) is proving to be a seminal development amongst this century's most productive and pervasive high-tech revolutions. Increased reliance on the internet of things (IoT) is one of the foremost trends, and the financial services industry is a major contributor to that trend. IoT's influence on our daily lives is noteworthy, and it has become imperative for financial services organizations to evolve to adapt to these changes. Digital devices have started to interconnect with each other and possibly with other peripheral entities. Owing to the explosion of these devices and digitization in the banking and financial services industry, businesses are discovering the possibility of IoT in finance to control data and to minimize the risk. This chapter focuses on the impact of internet of things on financial services. It discusses the various applications, trends, challenges, and risks associated with adoption of IoT by financial services institutions. This chapter also discusses Indian and global cases of application of internet of things by financial services institutions.

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.

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