# Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications

arXiv: https://arxiv.org/abs/2005.14658 · HTML: https://mlsanity.com/paper/2005.14658/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 9 of 9)

- [Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning](https://mlsanity.com/paper/2306.15585/) (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)
- [Relational Graph Neural Networks for Fraud Detection in a Super-App environment](https://mlsanity.com/paper/2107.13673/) (2021)
- [On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance](https://mlsanity.com/paper/2204.06122/) (2022)
- [Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data](https://mlsanity.com/paper/2102.09974/) (2021)
- [Enhancing User' s Income Estimation with Super-App Alternative Data](https://mlsanity.com/paper/2104.05831/) (2021)
- [Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection](https://mlsanity.com/paper/2111.03707/) (2021)
- [Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset](https://mlsanity.com/paper/2512.12783/) (2025)

## Abstract (arXiv)

In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments traditionally underserved by banks and financial institutions. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals, who are also the most likely to engage with alternative lenders. Furthermore, using the TreeSHAP method for Stochastic Gradient Boosting interpretation, our results also revealed interesting non-linear trends in the variables originating from the app, which would not normally be available to traditional banks. Our results represent an opportunity for technology companies to disrupt traditional banking by correctly identifying alternative data sources and handling this new information properly. At the same time alternative data must be carefully validated to overcome regulatory hurdles across diverse jurisdictions.