The financial domain is losing ground to rapid-developing fraud schemes. It puts intense pressure on organizations such as banks to find new approaches to tackle financial misconduct. This financial crime has always existed and is present in the financial industry. However, the rise of technology and the use of online transactions has enhanced the presence of the impact of fraud in this industry. The increase in financial fraud cases in a technological era result from a lack of inter-organizational synergy and the privacy concerns that entail by making data available. On top of the increasing fraud cases, organizations are exposed to increasing regulatory, financial, reputational, and legal risks. Hence, the financial crime industry and fraud prevention organizations must act on this threat. Therefore, these actors need to improve their current workflow continuously to keep up with the new developments. Different studies propose that it is a potential opportunity to take the chance and bundle data together to learn from the existing environment and improve their workflows and prediction models. However, the main concern is that parties are reluctant to share data as it involves confidential and sensitive data, which malicious parties can leverage and abuse. Also, the increasing focus on privacy protection regulations makes it complex and challenging to exchange data easily. The dataset that actors are providing will contain personally identifiable information, which in fact cannot be shared and proposed without any legitimate reason and is subjected to the data privacy regulations.
The existing set of techniques for sharing and analysing data securely, such as differential privacy, homomorphic encryption and federated learning have been proposed in studies and use cases are built in the real world. However, these techniques are insufficient and capable enough to facilitate multi-actor (data owners) data exchange and analysis. Secure Multiparty Computation, however, is capable of having multiple data owners securely perform a joint analysis. For this reason, this study has been focused on SMPC. In the case of the financial crime industry, it requires the involvement of multiple stakeholders sharing data simultaneously. Especially in the case of banks, it is essential to have bundled data for all transactions as these are connected.
To understand the purpose and the concept of the study, it is essential to have a basic understanding of SMPC. Secure Multiparty Computation is a cryptographic method for parties to mutually compute a function over all parties' acquired inputs while keeping the intake private throughout the entire process. Independent computation nodes will perform and provide analysis outcomes to designated parties. The concept of Secure Multiparty Computation has been studied by academia since the 1980s (Yao, 1982). However, the applications and introduction of SMPC are relativity novel to organizations and will not be immediately accepted. A technique such as SMPC will require participating parties to share a mutual interest and willingness to contribute continuously. It is uncertain if organizations will accept and adopt SMPC as mentioned before. Therefore, the study will also incorporate the concept and theory of collective action to understand the motives and the common goal for stakeholders in the anti-fraud industry to accept the technique and collaborate. A common goal, also known as a collective goal or interest, would create acceptance among the group. In this case, it will help to identify the factors and interests that influence an organization's decision to engage in collective action for developing MPC for fraud detection in the financial industry...

The growing aviation industry asks for innovative solutions to be able to handle the increasing amount of baggage. An example of such an innovative solution is Computer Vision Technology (CVT), which uses cameras to identify bags using data and artificial intelligence. The value of CVT for the baggage handling ecosystem is currently unknown. Besides this practical knowledge gap, a scientific gap is found as well. The majority of the literature on digital transformations has an organizational viewpoint and does not incorporate established ecosystem perspectives. It is unknown how a value proposition needs to be designed for a data driven technology to lead to a digital transformation of an established ecosystem.

A DSR approach is executed in a situated setting at Schiphol Airport to capture the value proposition of CVT for the baggage handling ecosystem by the identification of use cases. . The results show that the implementation of CVT provides value for Schiphol Airport, the baggage handling system provider, airlines, handlers, passengers, and society. The value proposition of CVT is the automated identification of bags based on visual images that provides thirteen use cases applicable throughout the whole baggage handling process, which leads to more autonomous processes, process improvement, the generation of more (types of) valuable data compared to the current identification techniques and can contribute to the achievement of sustainable goals if it replaces the current identification techniques.

The results not only contribute to the aviation industry, but the insights gained during the research are also valuable for future digital transformations within other established ecosystems. During the research, a lack of ecosystems’ support for the digital transformation was identified, caused by two factors. It was found that certain process choices had a positive influence on these two factors, which inspired the formulation of process guidelines. These guidelines contribute to the digital transformation knowledge base as they provide insights into how to enhance ecosystems support for digital transformations. In this way, it guides future digital transformation processes within established ecosystems. Furthermore, the research provides an approach to get a grip on a complex established ecosystem and a tool to specify data-driven use cases in combination with its implications for the established ecosystem. No tool existed to accommodate that. Therefore, a tool was constructed and used, which provided guidance on the use cases’ specification and could be valuable within future ideation processes of data-driven use cases for established ecosystems.

Over the last several years, the volume of data generated by the Internet of Things (IoT) has expanded at a rapid pace around the world. These often very detailed sensor data could be utilized by many different parties in order to improve services. This same tendency is seen in the automotive sector, where lots of data are gathered by newer and smarter cars. However, despite the fast data collection, car businesses seldom utilize this potential value. One way to make better use of automotive data is to share it with others via data marketplaces. Furthermore, data gathered by increasingly smarter cars is often very sensitive data. By aggregating and analyzing these car data, other parties can learn a lot about individual car users which could be experienced as unpleasant. Therefore, Multiparty Computation (MPC), a fairly new technology that facilitates encrypted and anonymized data sharing, tries to overcome this problem in order to increase worldwide data sharing. Academics and businesses realize that these data marketplaces have huge potential but most of the research done on MPC is very technical. As Multiparty Computation is a fairly unknown and not yet massively adopted application and also not specifically on data marketplaces, it is unclear to what extent car users are willing to provide their car data. The scientific problem herein is to find the consumers' preferences in the main measurable data sharing factors which influence consumers' data sharing behavior on MPC-enabled data marketplaces. To do so, we will employ a explorative approach by the Discrete Choice Modeling (DCM) method. Conclusions are drawn on how to improve these data marketplaces by revealing the relative importance of general data sharing factors. This paper aims to explore the consumers' preferences of MPC-enabled data marketplace factors to reveal the consumers' willingness to share automotive GPS data on data marketplaces in order to enhance data sharing and increase (road suggestion) products and thereby overall innovation. The findings revealed that the factors risk of data disclosure and benefit stay far more important to consumers in data sharing on MPC-enabled data marketplaces. Data control and trust (in terms of a herding effect) were least important to consumers, although consumers feel they are losing control over their data in this online era. This knowledge can then be utilized to create a data marketplace in which data providers are more likely to join.

Organizations share data for collective purposes: new opportunities are created to allow business enhancement. While new businesses contribute to economic development, valid reasons exist to inhibit data sharing (e.g. citizen privacy and sensitive information). Multi Party computation (MPC) provides a solution to these risks. However, MPC implementation remains limited, and we lack knowledge about the willingness to use MPC-enabled applications in organizational settings. The objective of this study is to investigate the effect of MPC on organizational willingness to contribute protected data for collective purposes. We ask: "To what extent does MPC affect organizational perception of the contribution of protected data?" From the quantitative assessment, MPC enhances organizational perceptions of data contribution and therefore significantly increases perceived trustworthiness and perceived security. Both of these aspects are found to be important and of approximately equal importance when considering contribution of protected data. That is, both are considered as the locus of willingness to contribute protected data through a web-based application. From the qualitative assessment, it is assumed that the positive contribution of MPC herein is because it allows data contribution independently from conventional data processors, which typically have access to raw data. The extent to which MPC increases perceptions depends on the extent to which an organization is able to assert the trustworthiness of the application and the security measure used by the application. MPC also affects perceived relative advantage. A weak correlation is reported between perceived relative advantage and willingness to contribute protected data, suggesting that relative importance is not perceived to be important as perceived trustworthiness and perceived security with respect to willingness to contribute protected data. Nevertheless, MPC also seems to enhance perceived relative advantage. Finally, although the relative advantage of MPC was not perceived as necessary, several findings are reported to further enhance the utility provided by an MPC application.