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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
The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics
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.
1568-4946
26-39
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Sarraute, Carlos
00c589d2-3b06-4172-88fb-df61068d7106
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Sarraute, Carlos
00c589d2-3b06-4172-88fb-df61068d7106
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Óskarsdóttir, María, Bravo, Cristian, Sarraute, Carlos, Vanthienen, Jan and Baesens, Bart (2019) The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 74, 26-39. (doi:10.1016/j.asoc.2018.10.004).

Record type: Article

Abstract

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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Accepted/In Press date: 1 October 2018
e-pub ahead of print date: 9 October 2018
Published date: January 2019

Identifiers

Local EPrints ID: 425295
URI: https://eprints.soton.ac.uk/id/eprint/425295
ISSN: 1568-4946
PURE UUID: 625ff4fd-08ea-4055-bfd1-5e21c61d7c68
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 12 Oct 2018 16:30
Last modified: 03 Dec 2019 05:11

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Contributors

Author: María Óskarsdóttir
Author: Cristian Bravo ORCID iD
Author: Carlos Sarraute
Author: Jan Vanthienen
Author: Bart Baesens ORCID iD

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