Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform’s capacity to build effective credit scoring. In recent years, many types of data are used for credit scoring to compensate for the lack of credit history data. Whether social network information can be used to strengthen financial institutions’ predictive power has received much attention in the industry and academia. The aim of this study is to test the reliability of social network information in predicting loan default. We extract borrowers’ social network information from mobile phones and then use logistic regression to test the relationship between social network information and loan default. Three machine learning algorithms—random forest, AdaBoost, and LightGBM—were constructed to demonstrate the predictive performance of social network information. The logistic regression results show that there is a statistically significant correlation between social network information and loan default. The machine learning algorithm results show that social network information can improve loan default prediction performance significantly. The experiment results suggest that social network information is valuable for credit scoring.

Lender trust is important to ensure the sustainability of P2P lending. This paper uses web crawling to collect more than 240,000 unique pieces of comment text data. Based on the mapping relationship between emotion and trust, we use the lexicon-based method and deep learning to check the trust of a given lender in P2P lending. Further, we use the Latent Dirichlet Allocation (LDA) topic model to mine topics concerned with this research. The results show that lenders are positive about P2P lending, though this tendency fluctuates downward with time. The security, rate of return, and compliance of P2P lending are the issues of greatest concern to lenders. This study reveals the core subject areas that influence a lender’s emotions and trusts and provides a theoretical basis and empirical reference for relevant platforms to improve their operational level while enhancing competitiveness. This analytical approach offers insights for researchers to understand the hidden content behind the text data.

The foundation and sustainable development of agricultural insurance involve accurately determining a premium and establishing a dynamic premium adjustment mechanism that matches the agricultural production risk. Based on the theoretical analysis of the impact of time–space risk adjustment on agricultural insurance ratemaking, we constructed a pure premium ratemaking model based on time-varying risk adjustment and a safety premium ratemaking model based on spatially dependent risk adjustment. Choosing the county grain area-yield index insurance (GAYI) in China as the research object, we obtained the following results: (1) the risk of grain yield per unit area (YPUA) and pure premium rate in most counties decreased significantly with time-varying adjustment, and we observed differences between regions; (2) grain’s spatially dependent risk has a strong negative adjustment effect on the loading factor, but the expansion of insurance underwriting can still rapidly reduce the safety premium rate, mainly due to the reduction in the spatially dependent risk; and (3) based on time-varying risk adjustment and underwriting expansion, the reduction effect of premium rates is obvious, which supports the sustainable commercial operation of agricultural insurance. These research results help to clarify the relationships of premium rates and provide implications on the sustainability of catastrophe management.

Physical activity in urban park contributes substantial health benefits for people. However, due to the absence of profile image systems, objective assessment of greenery supportiveness in park from people’s view is rare or even nonexistent. Manager and planner’s ability to manage and plan park landscapes effectively and efficiently is, therefore, limited. Typical smartphone, such as iPhone, has been widespread used to facilitate daily life. Image captured using panoramic mode in smartphone may be an alternative that can provide profile views of park landscape and greenery, yet no research seems available in literature. We investigated the feasibility of typical smartphone captured panoramic image and proposed a green vegetation extraction index (GVEI) for quantitatively depicting and monitoring the park greenery. A five-kilometer loop road network in Homestead, Florida, whose length and width are similar to the park trail, was selected as the test bed. The iPhone Xs was operated to take panoramic images of one hundred randomly selected investigation sites. Google Street View panoramic images acquired in the identical sites were downloaded and processed as comparison. The results demonstrated that the smartphone panoramic image is time-sensitive and well suited for assessing the trail-level greenery. The GVEI is an objective measurement indicator of trailside greenery. The smartphone panoramic image in combination with GVEI can be used to better understand the greenery supportiveness for physical activity and guide greenery planning in urban park.

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