The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics

by   María Óskarsdóttir, et al.

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.


page 20

page 22

page 23


Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending

Globally, two billion people and more than half of the poorest adults do...

Mobile Phone Usage Data for Credit Scoring

The aim of this study is to demostrate that mobile phone usage data can ...

Estimating Socioeconomic Status via Temporal-Spatial Mobility Analysis -- A Case Study of Smart Card Data

The notion of socioeconomic status (SES) of a person or family reflects ...

A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring

With the success of big data and artificial intelligence in many fields,...

Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment

Many households in developing countries lack formal financial histories,...

Analisis Kepuasan Pengguna Aplikasi Bintang Cash Credit Menggunakan Metode End User Computing Satisfaction (EUCS)

The use of android application technology has advanced rapidly in recent...

E.T.-RNN: Applying Deep Learning to Credit Loan Applications

In this paper we present a novel approach to credit scoring of retail cu...

Please sign up or login with your details

Forgot password? Click here to reset