Search Results (4)

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain. Full article

Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent management framework called the consumer default risk portrait (CDRP) to mitigate the default risks associated with online consumer loans. The CDRP framework combines traditional credit information and Internet platform data to depict the portrait of consumer default risks. It consists of four modules: addressing data imbalances, establishing relationships between user characteristics and the default risk, analyzing the influence of different variables on default, and ultimately presenting personalized consumer profiles. Empirical findings reveal that “Repayment Periods”, “Loan Amount”, and “Debt to Income Type” emerge as the three variables with the most significant impact on default. “Re-payment Periods” and “Debt to Income Type” demonstrate a positive correlation with default probability, while a lower “Loan Amount” corresponds to a higher likelihood of default. Additionally, our verification highlights that the significance of variables varies across different samples, thereby presenting a personalized portrait from a single sample. In conclusion, the proposed framework provides valuable suggestions and insights for financial institutions and Internet platform managers to improve the market environment of online consumer credit services. Full article

Wildfire spread models are commonly used to estimate fire exposure and risk, locate optimal fuel-treatment units, and study alternative management strategies. One of the most used algorithms to estimate fire spread is the minimum travel time (MTT). This algorithm requires a very time-consuming calibration process to produce reliable fire-spread estimates. Usually, the calibration process includes matching the simulated with observed fire sizes, frequently relying on tuning the fire duration. First, the user sets different duration classes based on the observed pattern and for each class sets a unique value, then runs the model and then assesses its performance. If the model fails to reproduce the historical fire size pattern, the user needs to redefine the fire duration values and repeat the entire process. Here, we present a new tool, specifically developed to assist the user during model calibration. This tool was developed for the command-line version of the MTT algorithm (FConstMTT) and was implemented in R software. We started by testing the optimal number of ignitions/fire seasons needed for the calibration and set it as default. The user can then specify multiple values per class of duration to be tested at the same time (instead of one single value per duration class). All the required input files are created for all the combinations of class durations and fire growth simulated for each combination. These combinations are ranked according to their accuracy, using the root mean square error statistic to compare simulated and observed fire size classes (as defined by the user). We demonstrate the potential of using this tool to speed up and improve the model’s calibration by applying it in four different study areas that are characterized by different fire regimes. We will gather feedback from the scientific community to further develop the tool. Full article

The aim of this study was to ascertain what can be done by the informal finance sector to close the credit gap in order to improve access to finance by SMEs. SMEs are the backbone of many economies as a result of generating employment and improving GDP. Despite playing such a major role in African economies, SMEs have been excluded from the financial systems. The informal finance sector plays a vital role by providing finance to small businesses. The study employed a literature survey with a primary focus on empirical studies that have been conducted in the African context. The study found that, generally, there are two circumstances under which most small businesses depend on informal finance. Firstly, informal finance is used as a last resort by SMEs that fail to access credit from the formal finance sector, owing to, among other issues, information asymmetry, lack of collateral security and perceived high default rates. Further, low financial literacy and the absence of credit bureaus in developing countries also contribute to the failure to access finance from formal institutions. Secondly, some entrepreneurs opt for informal finance even if they are eligible for formal finance as a result of its flexibility, convenience and simple administrative procedures. Notwithstanding the above benefits of informal finance, informal lenders are regarded as exploiting the clients by charging high interest rates. In addition, this sector suffers from limited resources; hence, it fails to fully service SMEs that require larger funding and are not eligible for formal finance. Invariably, all the studies that have been carried out confirm that access to finance is a major obstacle to the growth and development of SMEs. The development and empowerment of SMEs cannot be ignored as an important driver of the developmental agenda of most economies globally. The main policy recommendations that flow from this study, based on the policy syndrome of improving access to finance (financial inclusion) by the SME sector, include (1) the establishment of a suitable regulatory framework which will nurture the informal finance sector while promoting consumer protection, and (2) linking the formal and informal sector. On the other hand, SMEs should improve their risk management practices and also embrace FinTech platforms in order to access credit. Full article