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

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.

A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT

“…Moreover, a study is conducted to leverage alternative big data source and make unique combination of datasets, including call-detail records, credit, and debit account information of customers, to create scorecards for credit card applicants. The results show that combining call-detail records with traditional data in credit scoring models increases their performance when measured in AUC [24].…”

Influence of Internet-based Social Big Data on Personal Credit Reporting

“…Big data technology is becoming more and more widely used in the financial field, and big data credit reporting has also been paid more and more attention. Paper [4] defined "Big Data Credit Reporting from the Connotation and Composition of Credit" to define the essence of big data credit reporting is to use the big data technology as a means to help people record and judge credit value through the Internet, and comprehensively describe integrity and compliance Degree and compliance, big data credit has injected rich multi-dimensional credit data into the credit industry, providing more convenient and accurate credit information services, helping people without physical capital to find their own credit capital and value to realize their own aims. In essence, big data credit reporting is to make full use of the advantages of big data technology in credit reporting activities, such as large data processing volume, wide data dimensions, and dynamic display of data status, etc., in the business scope allowed by laws and regulations Within this, data information is collected, cleaned, and analyzed through big data technology, and services are provided to users through new perspectives and methods.…”

The Effect of Interfirm Financial Transactions on the Credit Risk of Small and Medium-Sized Enterprises

“…Network analyses, in contrast, have seen a growth in recent years, with significant progress being made both in the development of models and tools for network data and in the range of applications for which these methods have been found beneficial, from social network analyses to the modelling of financial, medical and biological networks (Kolaczyk and Csárdi, ). In the financial sphere, for example, social network data on customers have shown to be useful in credit scoring and fraud detection models (Van Vlasselaer et al ., ; Óskarsdóttir et al ., ).…”