Despite the many benefits of club-organized sports participation for children, sports participation is lower among children from low-income families than among those from middle- or high-income families. Social safety experienced by parents from low-income families is an important facilitator for parents to request financial support for their children’s sports participation. Therefore, the first aim of this study was to better understand parental social (un)safety in the context of acquiring financial support for children’s sports participation and how to create a safe social environment for low-income parents to request and receive this financial support. The second aim was to describe the co-creation process, which was organized to contribute to social safety solutions. To reach these goals, we applied a participatory action research method in the form of four co-creation sessions with professionals and an expert-by-experience, as well as a group interview with parents from low-income families. The data analysis included a thematic analysis of the qualitative data. The results showed that from the perspective of parents, social safety encompassed various aspects such as understandable information, procedures based on trust, and efficient referral processes. Sport clubs were identified as the primary source of information for parents. Regarding the co-creation process, the study found that stakeholders tended to overestimate parental social safety levels. Although the stakeholders enjoyed and learned from the sessions, differences in prior knowledge and a lack of a shared perspective on the purpose of the sessions made it challenging to collaboratively create solutions. The study’s recommendations include strategies for increasing parental social safety and facilitating more effective co-creation processes. The findings of this study can be used to inform the development of interventions that contribute to a social environment in which parents from low-income families feel safe to request and receive financial support for their children’s sports participation.
LINK
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.
Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
