---
title: XGBoost Framework for Fraud Detection in Mobile Payments
url: https://www.ml-quant.com/papers/repec/spr-infosf-v-25-y-2023-i-5-d-10-1007-s10796-022-10346-6/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:spr:infosf:v:25:y:2023:i:5:d:10.1007_s10796-022-10346-6
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10796-022-10346-6%3Bh%3Drepec%3Aspr%3Ainfosf%3Av%3A25%3Ay%3A2023%3Ai%3A5%3Ad%3A10.1007_s10796-022-10346-6
featured: 2023-10-12
citations: unknown
topic: ML & AI Methods
---


# XGBoost Framework for Fraud Detection in Mobile Payments

A proposed XGBoost-based framework for detecting fraud in mobile transactions was found to be most effective when combined with multiple unsupervised outlier detection algorithms.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10796-022-10346-6%3Bh%3Drepec%3Aspr%3Ainfosf%3Av%3A25%3Ay%3A2023%3Ai%3A5%3Ad%3A10.1007_s10796-022-10346-6
- Identifier: RePEc:spr:infosf:v:25:y:2023:i:5:d:10.1007_s10796-022-10346-6
- Released: 2023-10-12
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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