What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance

Cnudde, Sofie De; Moeyersoms, Julie; Stankova, Marija; Tobback, Ellen; Javaly, Vinayak; Martens, David

doi:10.1080/01605682.2018.1434402

Cited by 40 publications

(26 citation statements)

References 24 publications

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“…These findings were also confirmed by Freedman and Jin [20], with an additional caution that online ties on their own may not reveal true information about creditworthiness and may also be manipulated [21]. De Cnudde et al [22] developed credit scoring models for microfinance using social media network information extracted from Facebook accounts. Their results suggest that explicit networks of friends who interact are more predictive than of friends who do not, but implicit networks of people with similar behavior are better than both explicit friendship networks.…”

Section: Related Workmentioning

confidence: 71%

The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics

Óskarsdóttir

Bravo

Sarraute³

et al. 2019

Applied Soft Computing

144

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Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.

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Section: Related Workmentioning

confidence: 71%

The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics

Óskarsdóttir

Bravo

Sarraute³

et al. 2019

Applied Soft Computing

144

View full text Add to dashboard Cite

show abstract

“…Their framework showed that social networks can improve the prediction accuracy of borrowers' defaulting. De Cnudde et al [23] used online social network information extracted from Facebook accounts to build a credit scoring model. Their results show that social network information extracted from Facebook have predictive value.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some scholars have explored the role of social network information, but most only explored the correlation between online social network information and loan default. Although De Cnudde et al [23] extracted social network data from Facebook to increase the predictive ability, this method cannot be applied in many countries. For example, Facebook is blocked in some countries.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending

Niu

Ren

2019

Information

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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.

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“…It is needed in all online transactions. The individual's profile data on social networks such as Facebook, largely reflects the real data of the person who can be used to build creditworthiness [3]. Building a social credit system based on these ideas can improve all Traditional and online financial transactions.…”

Section: Introductionmentioning

confidence: 99%

“…Microfinance has been taken care of and how to ensure credit evaluation. In addition to traditional credit rating methods such as sociodemographic and credit data, other data from Facebook (like group, friends, and so on) has been extracted and used in a new model to automate the credit scoring process for microfinance [5]. The impact of using metrics based on social networks has been analyzed to give a score to customers is illustrated in [6].…”

Section: Introductionmentioning

confidence: 99%

The Feasibility of Implementing a Secure C2C Credit Scoring Platform

qabi¹,

Rajeh²

2021

IJACSA

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The continuous development of social media and online commerce, which permeates all aspects of our lives, leads to the need for a similar mechanism similar to the financial credit score in traditional business. Also, a realistic classification of users through social media to be used in all aspects of the relationships between users and some of them or between them and organizations is needed. In this article a new metrics to classify users according to their creditworthiness of the transactions that take place through the Internet is established. The object from this aricle design a social credit system model (SCSM) based on these new metrics. How to deal on the Internet, attacking people on social media, violating the privacy of people and others. Also Buying and selling operations, executing purchase and sale orders, paying amounts of money easily and quickly, and so on. These data and their degree of importance were determined according to several questionnaires directed to several segments of society. This creditworthiness can be used in banks, Uber, Online transactions and so on.

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What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance

Cited by 40 publications

References 24 publications

The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics

The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics

Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending

The Feasibility of Implementing a Secure C2C Credit Scoring Platform

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