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.
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The rise of financial technology (fintech) driven business models in banking poses a challenge for financial regulators. While the positive effects on the banking sector in terms of greater diversity and competition are generally recognized and encouraged by regulators, the nature of fintech business models may increase the risk of financial instability. Regulators are exploring ways to resolve this dilemma. The paper in hand makes a contribution to the literature by providing a framework for resolving the dilemma that is evaluated in the context of the regulatory response to the rise of fintech credit in the Netherlands. The semi-structured interviews which we conducted with four senior Dutch regulators resulted in three areas that–from their perspective–required urgent action: fintech credit companies need to lower the risk of overlending, increase pricing transparency, and improve lending standards. These findings were confirmed by the results of they survey among fintech credit clients. The current regulatory response to the rise of fintech in banking in the Netherlands provides an interesting case study that delineates the features of the future regulation of fintech in banking.
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Abstract Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has challenged healthcare globally. An acute increase in the number of hospitalized patients has neces‑ sitated a rigorous reorganization of hospital care, thereby creating circumstances that previously have been identifed as facilitating prescribing errors (PEs), e.g. a demanding work environment, a high turnover of doctors, and prescrib‑ ing beyond expertise. Hospitalized COVID-19 patients may be at risk of PEs, potentially resulting in patient harm. We determined the prevalence, severity, and risk factors for PEs in post–COVID-19 patients, hospitalized during the frst wave of COVID-19 in the Netherlands, 3months after discharge. Methods: This prospective observational cohort study recruited patients who visited a post-COVID-19 outpatient clinic of an academic hospital in the Netherlands, 3months after COVID-19 hospitalization, between June 1 and October 1 2020. All patients with appointments were eligible for inclusion. The prevalence and severity of PEs were assessed in a multidisciplinary consensus meeting. Odds ratios (ORs) were calculated by univariate and multivariate analysis to identify independent risk factors for PEs. Results: Ninety-eight patients were included, of whom 92% had ≥1 PE and 8% experienced medication-related harm requiring an immediate change in medication therapy to prevent detoriation. Overall, 68% of all identifed PEs were made during or after the COVID-19 related hospitalization. Multivariate analyses identifed ICU admission (OR 6.08, 95% CI 2.16–17.09) and a medical history of COPD / asthma (OR 5.36, 95% CI 1.34–21.5) as independent risk fac‑ tors for PEs. Conclusions: PEs occurred frequently during the SARS-CoV-2 pandemic. Patients admitted to an ICU during COVID19 hospitalization or who had a medical history of COPD / asthma were at risk of PEs. These risk factors can be used to identify high-risk patients and to implement targeted interventions. Awareness of prescribing safely is crucial to prevent harm in this new patient population.
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