Using Credit Scores to Understand Predictors and Consequences of Disease

Financial illiteracy is a systemic issue across the country, especially among lower-income individuals in urban communities. This low level of financial literacy often leads to higher levels of debt, lower credit scores, less wealth accumulation, and poor retirement planning. Increasing financial literacy in these priority populations can be effective in combatting some of these negative financial outcomes. This study emerged from a partnership between community organizations in a large urban metropolitan area and scholars from diverse disciplinary backgrounds. Guided by formative research principles, this manuscript reports on research findings derived from several focus groups with community members. These focus groups helped to identify existing perceived financial knowledge levels, categorize barriers to enhancing financial literacy, and illuminate potentially pathways to effective financial literacy program development.

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Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

Asia Pacific Journal of Marketing and Logistics ◽

10.1108/apjml-12-2020-0872 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hongming Gao ◽

Hongwei Liu ◽

Haiying Ma ◽

Cunjun Ye ◽

Mingjun Zhan

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Scoring System ◽

Credit Scoring ◽

Prediction Performance ◽

Content Type ◽

Connected Subgroup ◽

Source Data ◽

Credit Scores

PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

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Functional Consequences of Disease-Associated Mutations in TNFR1 Elucidated by Transcriptome Analysis

Advances in Experimental Medicine and Biology - Advances in TNF Family Research ◽

10.1007/978-1-4419-6612-4_47 ◽

2010 ◽

pp. 461-470

Author(s):

Susana L. Rebelo ◽

Paul M. Radford ◽

Susan E. Bainbridge ◽

Ian Todd ◽

Patrick J. Tighe

Keyword(s):

Transcriptome Analysis ◽

Functional Consequences ◽

Consequences Of Disease

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Corporate parents, initial legitimacy, and resource acquisition in small and medium firms: An empirical examination

New England Journal of Entrepreneurship ◽

10.1108/neje-14-01-2011-b002 ◽

2011 ◽

Vol 14 (1) ◽

pp. 23-34 ◽

Cited By ~ 1

Author(s):

Gregory Murphy ◽

Neil Tocher

Keyword(s):

Firm Size ◽

Small Firms ◽

Small And Medium Enterprises ◽

Empirical Test ◽

Resource Acquisition ◽

The Internet ◽

Empirical Examination ◽

Credit Scores ◽

Medium Enterprises ◽

Medium Firms

Small and medium enterprises (SMEs) commonly struggle to acquire needed financial, human, and technological resources. The above being stated, recent scholarly research argues that SMEs that are able to successfully navigate the legitimacy threshold are better able to gather the resources they need to survive and grow. This article provides an empirical test of that claim by examining whether the presence of a corporate parent positively influences SME resource acquisition. Results of the study show that SMEs with corporate parents, when compared to like-sized independent SMEs, have higher credit scores, have more complete management teams, use more computers, and are more likely to be on the Internet. These differences are most pronounced for very small firms and diminish in significance as firm size increases. Study implications include the notion that presence of a corporate parent likely represents a successful navigation of the legitimacy threshold, positively increasing SME resource acquisition.

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