Abstract: Credit scoring is widely used by financial institutions for default prediction, however, a significant portion of online credit loan customers have inadequate or unverifiable credit histories, making it difficult for financial institutions to make effective credit decisions. Since the widespread use of smartphones and the popularity of mobile applications, it is worth investigating whether mobile application usage behaviors (App behaviors) of customers can effectively predict online loan defaults. This paper proposes a combined algorithm of CNN and LightGBM, and establishes credit scoring models with App behaviors to evaluate the default risk of online credit loans based on logistic regression, LightGBM,…CNN and the combined algorithm, respectively. The experimental results suggest that App behaviors have an obvious effect on the default prediction of customers applying for online credit loans, and the combined model outperforms the other models in terms of the area under the curve (AUC). Furthermore, integrated credit scoring models are developed by combining App behaviors with traditional scoring features. A comparison of the integrated models and the traditional scoring model indicates that the integrated models have achieved a significant improvement in classification performance and App behaviors can be a powerful complement to the traditional credit scoring model.
Show more
Keywords: Credit scoring, online credit loans, mobile application usage behaviors, logistic regression, combined model
Abstract: The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers’ loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design…the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application.
Show more
Keywords: Fraud prediction, mobile application usage behaviors, extreme gradient boosting, long short-term memory, grouped trees, weighted ensemble algorithm
Abstract: BACKGROUND: Drug resistance in clinical cancer treatment has become an issue. OBJECTIVE: We focus on abnormally expressed lncRNAs in glioma and investigating the function of PVT1. METHODS: The paclitaxel-resistant glioma cells SHG-44 RE was obtained through screening the SHG 44 cells that were cultured in medium containing a certain concentration of paclitaxel. Cell survival of SHG 44 RE and SHG 44 cells under the treatment of paclitaxel was detected by MTT assay. The aberrant expressed lncRNAs were screened out with microarray analysis. Further qRT-PCR was utilized to validate the expression of lncRNA PVT1 in the two cells. After manipulating the expression of PVT1,…cell viability and apoptosis were measured by MTT and flow cytometry respectively. RESULTS: LncRNA PVT1 was overexpressed in glioma cells SHG-44 RE compared with parent SHG-44 cells. Down-regulation of lncRNA PVT1 inhibited the SHG-44 RE cell viability and increased glioma SHG-44 RE cells apoptosis after paclitaxel treatment, suggesting that inhibition of lncRNA PVT1 improved paclitaxel sensibility in human glioma cells. CONCLUSION: Down-regulation of PVT1 could enhance chemosensitivity of paclitaxel, induce apoptosis of glioma cells and noteworthy inhibit glioma cells proliferation. Our findings of PVT1 could contribute to attenuate paclitaxel resistance in clinical medicine.
Show more
Abstract: A recent study sequenced the full coding region of SORL1 in 1,255 early-onset Alzheimer’s disease (EOAD) cases and 1,938 control individuals, and investigated the contribution of genetic variability in SORL1 to EOAD risk in a European cohort. This study identified six common variants and five low frequency variants in the SORL1 coding sequence. However, none of these 11 variants was significantly associated with EOAD risk after adjusting for multiple testing. We consider whether these 11 SORL1 variants identified in European EOAD contribute to late-onset Alzheimer’s disease (LOAD) risk in individuals of European ancestry. Here, we investigated these 11 SORL1 variants…identified in European EOAD and LOAD risk in individuals of European ancestry using a large-scale LOAD GWAS. Our results indicate that three genetic variants rs2070045, rs2276412, and rs17125548 as well as their tagged genetic variants contribute to LOAD risk in European population. We further investigate whether these variants could affect SORL1 expression using multiple expression quantitative trait loci (eQTLs) datasets. Our findings suggest that three genetic variants rs2070045, rs1699102, and rs3824968 could significantly regulate SORL1 expression in human brain tissues. We believe that our findings further provide important supplementary information about the involvement of the SORL1 variants in LOAD risk.
Show more
Keywords: Alzheimer’s disease, genome-wide association studies, SORL1