Fraud Prevention in Banking: Innovative Techniques for Detecting Payment Fraud

Title

Fraud Prevention in Banking: Innovative Techniques for Detecting Payment Fraud

Creator

Anthraper, Joseph Abraham; Upreti, Kamal

Description

Fraud detection in banking remains one of the most critical challenges, as fraudulent patterns continue changing to avoid detection. Classic rule-based methods provide a basis approach but often lead to high rates of false positives and negatives, which limiting their efficiency. Due to the rapid growth of fraud particularly in Banking Payments, tackling this challenge has become imperative. To this end, we employ the Banksim dataset-a synthetic tool that replicates the various payment behavior of customers- to assess a number of machine learning models, Support Vector Machines, Random Forest, Logistic Regression, AdaBoost and Decision trees. Our model evaluation, using confusion matrices and classification reports, demonstrates the ability of these approaches to provide precision and reliability in detecting fraudulent transactions. This research contributes to enhancing the reliability and integrity of banking services through fraud payment detection improvements. 2025 IEEE.

Source

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.144-151

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

banking; Banksim dataset; Fraud detection; machine learning

Coverage

Anthraper J.A., Christ (Deemed to be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India; Upreti K., Christ (Deemed to be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152749-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IC363308.2025.10957594

https://www.scopus.com/pages/publications/105003909444?origin=resultslist