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This study employs LendingClub data in the field of personal credit risk control as an illustrative case. Various data mining models, and support vector machine, are utilized for training purposes. Additionally, a Stacking model is integrated into the analysis to forecast customer default likelihood. Subsequently, lending decisions are made in accordance with these predictions. The outcomes indicate a reduction in customer default rates compared to scenarios without the application of data mining models, thereby achieving our goal of risk control.

This paper presents a stylized model to determine the optimal strategy for the integrated supplier-retailer inventory model under the condition that both the supplier and retailer have adopted a trade credit strategy. By analyzing the total channel profit function, we develop an algorithm to simultaneously determine the retailer's optimal order quantity and the number of shipment per production run from the supplier to the retailer. Our results demonstrate that the trade credit strategy is effective to supply chain system performance when customers are sensitive to the credit period length offered by the retailer. Moreover, when customers are sensitive to the credit period, if the retailer conveys partial advantage gained from the trade credit offered by the supplier to customers by suitably adjusting the customer's credit period then the entire system and every channel partner can benefit.

Online social networks (OSNs) are a terrifically emerging platform for information dissemination around the world. Like other settings, acceptance and adoption of OSNs among the individual capital market investors are extensive. The study developed a conceptual model for behavioural finance integrating a technology acceptance model (TAM) and valence framework from the information systems and marketing disciplines, respectively. The integrated model added some persuasive constructs from social capital and diffusion innovation theory with a view to explore the key factors swaying investors’ intention to adopt and use the OSN’s services. By using an online and offline structured questionnaire, 510 data were collected from individual capital market investors in Bangladesh. Structural Equation Modelling (SEM) was used for data analysis. The study determined that the proposed integrated model with additional constructs outperformed other models. Perceived usefulness (PU), perceived enjoyment (PE), trust and personal innovativeness in IT (PIIT) had a substantial sway on the investor’s intention to use OSNs. Hedonic value is more robust predictor of intention to use OSNs than utilitarian value. Intention to use properly mediated the relationships and had strong significant impact on investor’s investment decision. But perceived ease of use (PEOU) and perceived risk had no direct significant effect on intention to use. PEOU had significant impact on intention to use through PU and PE. Gender moderated the relationships of different constructs with the intention to use OSNs for investment decisions in the capital market. It contributes knowledge by including the integration of different models in stock market perspectives and the inclusion of technological aspect in the behavioural finance literature. The findings of the study will also succor different firms and regulatory authorities to adopt OSNs as an information dissemination platform.

Chronic Kidney Disease (CKD) are a universal issue for the well-being of people as they result in morbidities and deaths with the onset of additional diseases. Because there are no clear early symptoms of CKD, people frequently miss them. Timely identification of CKD allows individuals to acquire proper medications to prevent the development of the diseases. Machine learning technique (MLT) can strongly assist doctors in achieving this aim due to their rapid and precise determination capabilities. Many MLT encounter inappropriate features in most databases that might lower the classifier’s performance. Missing values are filled using K-Nearest Neighbor (KNN). Adaptive Weight Dynamic Butterfly Optimization Algorithm (AWDBOA) are nature-inspired feature selection (FS) techniques with good explorations, exploitations, convergences, and do not get trapped in local optimums. Operators used in Local Search Algorithm-Based Mutation (LSAM) and Butterfly Optimization Algorithm (BOA) which use diversity and generations of adaptive weights to features for enhancing FS are modified in this work. Simultaneously, an adaptive weight value is added for FS from the database. Following the identification of features, six MLT are used in classification tasks namely Logistic Regressions (LOG), Random Forest (RF), Support Vector Machine (SVM), KNNs, Naive Baye (NB), and Feed Forward Neural Network (FFNN). The CKD databases were retrieved from MLT repository of UCI (University of California, Irvine). Precision, Recall, F1-Score, Sensitivity, Specificity, and accuracy are compared to assess this work’s classification framework with existing approaches.