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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Vietnamese farmed pangasius products have experienced major price fluctuations over numerous crop cycles exposing this important export commodity to risk and uncertainty. This study focuses on two distinct areas of risk. First, this study seeks to measures the farm-gate price volatility, explores farmers’ perceptions about the price volatility risk related to the input sourcing and output selling activities, and quantifies the effectiveness of price volatility risk management strategies. Second, the relationships between farm and farmer socioeconomic characteristics, perceptions about the price volatility risk and management strategies are also examined. To respond to these research questions, a focus group workshop was conducted with 29 stakeholders and a structured survey with 92 farmers located in the provinces of An Giang, Can Tho, and Dong Thap of the Vietnamese Mekong Delta. Results illustrated that the pangasius farm-gate price fluctuates significantly over time. Vietnamese pangasius farmers are mainly concerned about the volatilities of input and output prices, additionally with the instability of the volume input supply, instability in demand volume, and weak legislation on sale contracts. Results further indicate that price volatility risk management strategies are less effective in practically protecting farmers against price volatility risks. Farmers with higher education knowledge opt for fully integrated farms and are less concerned about the output price volatility risk. Promoting risk-sharing schemes such as contract farms or insurance for farmers with higher educational levels and being accessible to credit could support Vietnamese pangasius farmers toward increasing the sustainability of pangasius production.
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AI is een uitstekende tool om financiële fraudedetectie en de beoordeling van credit risks te versnellen, zo bleek uit afstudeeropdrachten die studenten van de masteropleiding AI aan de HvA afgelopen semeser bij de financiële opleidingen hebben uitgevoerd. Maar we zien soms dat beslissingen op basis van AI-systemen te rigoureus worden genomen.
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The world of student associations, is not all what it seems to be. Here, like in the corporate boardroom, we find a world of personal ambition that drive unproductive acquisitions and other unwanted managerial behavior. Agency problems as studied by Jensen & Meckling (1976) and eloquently summarized by Gordon Gekko (1987) are major causes of the credit crisis of 2008.
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Editorial on the Research Topic "Leveraging artificial intelligence and open science for toxicological risk assessment"
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Background: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration. Results: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56-0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63-0.73; PHL was 0.658). Discussion: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.
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Financial constraints and risk taking are two well-established determinants of firm performance, however, no research analyzes how these variables are connected in the context of a high risk environment. Using data from microfinance clients in Tanzania, we derive a novel financial constraints measure and incorporate a psychometric risk taking scale. Results confirm the importance of access to finance and risk attitudes for business development. Also, we provide preliminary evidence for an interaction between financial constraints and risk taking. Financial constraints “throw sand in the wheels” and protect risk taking entrepreneurs from the negative impact of risk taking on microenterprise performance.
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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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Creditmanagement lijkt een onderwerp dat we inmiddels wel kennen. Toch is het vakgebied nog volop in ontwikkeling, zowel vanuit de praktijk als vanuit de theorie. Waar nu met name de focus ligt op tijdgedreven creditmanagement (gebaseerd op days sales outstanding), betogen wij in dit artikel dat creditmanagement veel beter gebaseerd kan worden op de risico’s en kosten die aan leverancierskrediet zijn verbonden.
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