While criminality is digitizing, a theory-based understanding of the impact of cybercrime on victims is lacking. Therefore, this study addresses the psychological and financial impact of cybercrime on victims, applying the shattered assumptions theory (SAT) to predict that impact. A secondary analysis was performed on a representative data set of Dutch citizens (N = 33,702), exploring the psychological and financial impact for different groups of cybercrime victims. The results showed a higher negative impact on emotional well-being for victims of person-centered cybercrime, victims for whom the offender was an acquaintance, and victims whose financial loss was not compensated and a lower negative impact on emotional well-being for victims with a higher income. The study led to novel scientific insights and showed the applicability of the SAT for developing hypotheses about cybercrime victimization impact. In this study, most hypotheses had to be rejected, leading to the conclusion that more work has to be done to test the applicability of the SAT in the field of cybercrime. Furthermore, policy implications were identified considering the prioritization of and approach to specific cybercrimes, treatment of victims, and financial loss compensation.
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This experimental study with a pre-post and follow-up design evaluates the financial education program “SaveWise” for ninth grade students in the Netherlands (n = 713). SaveWise adopts a holistic approach, emphasizing action rather than mere cognition. Benefitting from explicit instruction embedded in real-life contexts, students in the program set a personal savings goal and are coached on how to achieve it. The short-term treatment results indicated that SaveWise expanded the students’ level of financial knowledge; encouraged their intentions to save more, spend less and earn an income; and broadly improved their financial and savings behavior. The program demonstrated that it could serve as an effective and low-cost method to enhance the financial literacy of pre-vocational students, a financially vulnerable group. Although long-term effects were expressed only through financial socialization, this study offers evidence linking curricula to increased knowledge and improved behavior for a specific sample of students.
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Whitepaper: The use of AI is on the rise in the financial sector. Utilizing machine learning algorithms to make decisions and predictions based on the available data can be highly valuable. AI offers benefits to both financial service providers and its customers by improving service and reducing costs. Examples of AI use cases in the financial sector are: identity verification in client onboarding, transaction data analysis, fraud detection in claims management, anti-money laundering monitoring, price differentiation in car insurance, automated analysis of legal documents, and the processing of loan applications.
The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
