Fraud Detection in Electronic Payments
Project Summary
| Category | Fintech |
|---|---|
| Customer | Evendor / Fundación BBVA |
| Period | 2015-06-01 to 2016-12-31 |
Overview
This research project applied advanced techniques from artificial intelligence (AI) and data science to the problem of detecting fraud in electronic payment systems, with a particular focus on credit card transactions.
In those days, commercial AI-based tools were still in their infancy, and many anti-fraud systems were still a combination of rule based and very basic statistical filters. Our work involved analyzing over 150 million real transactions collected over one year by a first tier bank, to identify statistical traces and behavioural patterns associated with fraudulent activity. By leveraging AI-driven models, the research aimed to improve the ability of financial institutions to decide in real time whether a transaction should be blocked.
The study tackled significant challenges typical of fraud detection—such as extremely imbalanced data (with approximately one fraudulent transaction per 6 000 legitimate ones), evolving fraud strategies that change over time, and the need to model the decision-making utility for both the bank and the fraudster. The project developed new algorithms that substantially improved model efficiency compared to existing approaches.
In addition to its technical contributions, the project emphasised training early-stage researchers in statistical learning and data science, responding to strong market demand for these skills and the lack of formal academic programmes in this area at the time.
Due to confidentiality clauses, we were unable to publish publicly available results on this research.
Media Coverage
- “Matemáticas antirrobo y otras cuatro ideas para mejorar el mundo”, Diario EL País (30/07/2015)
- “La Fundación BBVA financia un proyecto basado en matemáticas que permitirá adelantarse al fraude bancario” ICMAT (29/07/2015) link
Projects and Contracts
- Artificial Intelligence and Data Science: Applications in Payment Fraud Detection, Leonardo Scholarship, Fundación BBVA. link Red Leonardo
- Sum: 40.000 EUR (2015-2016)
Contract and Funding
- Learning for fraud detection in electronic payments.
- Contract Art. 83 between Evendor Engineering SL and Univ. Complutense de Madrid.
- PI: David Gómez-Ullate (UCM-ICMAT), 01/06/2015 - 31/12/2015, Sum: 20.000 EUR.
