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

arXiv: https://arxiv.org/abs/2002.09931 · HTML: https://mlsanity.com/paper/2002.09931/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 21 of 21)

- [Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams](https://mlsanity.com/paper/2304.10740/) (2023)
- [Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning](https://mlsanity.com/paper/2306.15585/) (2023)
- [The AI Revolution: Opportunities and Challenges for the Finance Sector](https://mlsanity.com/paper/2308.16538/) (2023)
- [Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction](https://mlsanity.com/paper/2402.00299/) (2024)
- [On the combination of graph data for assessing thin-file borrowers' creditworthiness](https://mlsanity.com/paper/2111.13666/) (2021)
- [Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity](https://mlsanity.com/paper/2308.02680/) (2023)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)
- [On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance](https://mlsanity.com/paper/2204.06122/) (2022)
- [FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling](https://mlsanity.com/paper/2601.11134/) (2026)
- [Weight-of-evidence 2.0 with shrinkage and spline-binning](https://mlsanity.com/paper/2101.01494/) (2021)
- [Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data](https://mlsanity.com/paper/2102.09974/) (2021)
- [INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2307.08131/) (2023)
- [The Double-Edged Sword of Big Data and Information Technology for the Disadvantaged: A Cautionary Tale from Open Banking](https://mlsanity.com/paper/2307.13408/) (2023)
- [PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations](https://mlsanity.com/paper/2512.22305/) (2025)
- [Federated Artificial Intelligence for Unified Credit Assessment](https://mlsanity.com/paper/2105.09484/) (2021)
- [Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection](https://mlsanity.com/paper/2111.03707/) (2021)
- [Assessment of creditworthiness models privacy-preserving training with synthetic data](https://mlsanity.com/paper/2301.01212/) (2022)
- [A machine learning workflow to address credit default prediction](https://mlsanity.com/paper/2403.03785/) (2024)
- [A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks](https://mlsanity.com/paper/2510.09407/) (2025)
- [Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset](https://mlsanity.com/paper/2512.12783/) (2025)
- [The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?](https://mlsanity.com/paper/2512.18390/) (2025)

## Abstract (arXiv)

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.