Adaptive Systems for Fraud Detection in Financial Transactions: A Survey on Multi-Modal Biometrics and Real-Time Analytics

Shubhangi Vairagar, Vaishnavi Babar

2025

Abstract

The paper takes a revolutionary approach to countering financial fraud through adaptive fraud detection using multi-modal behavioral biometrics and real-time predictive analytics. Current systems are based on static rules and historical data that fail to counter modern and sophisticated techniques of fraud. This system builds an adaptive, all-inclusive user profile by including behavioral biometrics such as typing patterns, mouse movements, and emotional cues captured through facial recognition. Advanced machine learning algorithms improve on anomaly detection, enabling a system to adapt to in real-time changes in behavior of the user and changing fraud patterns, thereby strongly reducing false positives while the detection rates are improved. **Real-time predictive analytics** identify and stop fraudulent transactions prior to their occurrence, thus reducing monetary losses. The model will also use **blockchain technology**, where suspicious transactions can be logged safely for transparent audit purposes, hence increasing trust levels and transparency in transactions. The system adds layers of precision to fraud detection by utilizing live behavioral data for dynamic risk assessments. Its explainable AI mechanisms are transparent, which fosters user trust, and its adaptability supports resilience against evolving fraud tactics. The proposed system marks a significant leap forward, promising a safer and more efficient environment for financial transactions, ultimately revolutionizing fraud prevention strategies in the financial sector.

Paper Citation

in Harvard Style

Vairagar S. and Babar V. (2025). Adaptive Systems for Fraud Detection in Financial Transactions: A Survey on Multi-Modal Biometrics and Real-Time Analytics. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 321-328. DOI: 10.5220/0013615200004664

in Bibtex Style

@conference{incoft25,
author={Shubhangi Vairagar and Vaishnavi Babar},
title={Adaptive Systems for Fraud Detection in Financial Transactions: A Survey on Multi-Modal Biometrics and Real-Time Analytics},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={321-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013615200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Adaptive Systems for Fraud Detection in Financial Transactions: A Survey on Multi-Modal Biometrics and Real-Time Analytics
SN - 978-989-758-763-4
AU - Vairagar S.
AU - Babar V.
PY - 2025
SP - 321
EP - 328
DO - 10.5220/0013615200004664
PB - SciTePress