Automated data collection for credit score calculation based on financial transactions and social media

Lohokare, Jay; Dani, Reshul

doi:10.1109/etiict.2017.7977024

Cited by 16 publications

(6 citation statements)

References 3 publications

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“…Ref. [21] proposed the development of a mobile application to collect data from social media for credit scoring to simplify data collection for financial institutions. Ref.…”

Section: Alternative Data In Credit Scoringmentioning

confidence: 99%

Spatiotemporal Integration of Mobile, Satellite, and Public Geospatial Data for Enhanced Credit Scoring

et al. 2021

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Credit scoring of financially excluded persons is challenging for financial institutions because of a lack of financial data and long physical distances, which hamper data collection. The remote collection of alternative data has the potential to overcome these challenges, enabling credit access for such individuals. Whereas alternative data sources such as mobile phones have been investigated by previous researchers, this research proposes the integration of mobile-phone, satellite, and public geospatial data to improve credit evaluations where financial data are lacking. An approach to integrating these disparate data sources involving both spatial and temporal analysis methods such as spatial aggregation was employed, resulting in various data combinations. The resulting data sets were used to train classifiers of varying complexity, from logistic regression to ensemble learning. Comparisons were based on various performance metrics, including accuracy and the area under the receiver operating-characteristic curve. The combination of all three data sources performed significantly better than mobile-phone data, with the mean classifier accuracy and F1 score improving by 18% and 0.149, respectively. It is shown how these improvements can translate to cost savings for financial institutions through a reduction in misclassification errors. Alternative data combined in this manner could enhance credit provision to financially excluded persons while managing associated risks, leading to greater financial inclusion.

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“…Ref. [21] proposed the development of a mobile application to collect data from social media for credit scoring to simplify data collection for financial institutions. Ref.…”

Section: Alternative Data In Credit Scoringmentioning

confidence: 99%

Spatiotemporal Integration of Mobile, Satellite, and Public Geospatial Data for Enhanced Credit Scoring

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For instance, an applicant's college, her use of capital letters in applications (whereby the use of all caps writing is interestingly a warning sign) and social media data [26], including online tracking and behavioural profiling [79,81]. Moreover, data harvested by specific apps from smartphones might be included [82]. Furthermore, other network-based data is included, developing a social credit score based on the individuals' position in a social structure [79].…”

Section: What Is Credit Scoring?mentioning

confidence: 99%

“…Respondents who would not consider such a system acceptable voiced concerns such as the violation of privacy, the accuracy of online data representing a person, and the irrelevance of online habits and behaviours for an individual's creditworthiness [89]. The exploration and increasing use of alternative data sources for credit scoring, including social media data or information from smartphones [79,82,83,90], is perceived rather critically in research [27,83] and by most participants as inquired by the Pew Research Center [89]. Users and customers (people who these systems decide about) seem to have a different attitude towards ADM systems than managers and executives of companies or institutions who decide about these systems.…”

Section: Human-centric Adm Systems For Credit Scoringmentioning

confidence: 99%

Trust, Automation Bias and Aversion: Algorithmic Decision-Making in the Context of Credit Scoring

Gsenger¹,

Strle²

2021

Interdisciplinary Description of Complex Systems

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Algorithmic decision-making (ADM) systems increasingly take on crucial roles in our technology-driven society, making decisions, for instance, concerning employment, education, finances, and public services. This article aims to identify peoples' attitudes towards ADM systems and ensuing behaviours when dealing with ADM systems as identified in the literature and in relation to credit scoring. After briefly discussing main characteristics and types of ADM systems, we first consider trust, automation bias, automation complacency and algorithmic aversion as attitudes towards ADM systems. These factors result in various behaviours by users, operators, and managers. Second, we consider how these factors could affect attitudes towards and use of ADM systems within the context of credit scoring. Third, we describe some possible strategies to reduce aversion, bias, and complacency, and consider several ways in which trust could be increased in the context of credit scoring. Importantly, although many advantages in applying ADM systems to complex choice problems can be identified, using ADM systems should be approached with caree.g., the models ADM systems are based on are sometimes flawed, the data they gather to support or make decisions are easily biased, and the motives for their use unreflected upon or unethical.

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“…Marqués et al [5] illustrated different resampling algorithms to resolve the core issue of class imbalance, and discussed the application of logistic regression as well as support vector machine in credit scoring. Lohokare et al [6] explained the application of artificial neural networks in credit scoring based on social media data collected from SMS in smartphone. He et al [7] compared logistic regression, decision trees and neural networks to verify the feasibility and validity of credit scoring models applied in credit evaluation.…”

Section: Introductionmentioning

confidence: 99%

Application and Legal Governance of Computational Technologies in Credit Investigation: An Interdisciplinary Perspective

Zhang

Xie

2022

Discrete Dynamics in Nature and Society

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The purpose of this interdisciplinary study is to investigate the applications of modern computational technologies in the field of credit investigation and discuss the related legal governance measurements in order to unblock the bottlenecks in the future development of credit investigation market. By analyzing the computational technologies and algorithms most commonly used in credit data collection and data storage, data transmission and data access, data analysis and processing, data calculation, result output and effect evaluation, this paper summarizes and proposes a unified general process of modern credit investigation, pointing out that in this general process, low data quality, privacy violation, algorithmic bias are the main challenges in the big data era, and countermeasures like data quality control, privacy protection, and algorithm governance need to be to be taken seriously into account in order to further explore the great potential of the credit investigation under the legal framework.

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Automated data collection for credit score calculation based on financial transactions and social media

Cited by 16 publications

References 3 publications

Spatiotemporal Integration of Mobile, Satellite, and Public Geospatial Data for Enhanced Credit Scoring

Spatiotemporal Integration of Mobile, Satellite, and Public Geospatial Data for Enhanced Credit Scoring

Trust, Automation Bias and Aversion: Algorithmic Decision-Making in the Context of Credit Scoring

Application and Legal Governance of Computational Technologies in Credit Investigation: An Interdisciplinary Perspective

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