Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Safescan: Proactive Fraud Detection In Digital Payments Using Ml

Authors
B. Sudha Madhuri1, *, Jillidimudi Lalitha Vasavi1, Krishna Vineetha1, Patnaik Kuppili1, Munakala Priya1, Panduri Rakshitha Ratna Sai1
1Department of IT, Vignan’s Institute of Engineering for Women, Visakhapatnam, AP, India
*Corresponding author. Email: sudhamadhuri10863@gmail.com
Corresponding Author
B. Sudha Madhuri
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_35How to use a DOI?
Keywords
Python; Machine Learning; Flask Integration; Scikit; QR Scanner; Database; Transaction Patterns; Fraud Detect
Abstract

Fraud in digital payments poses a significant threat to user security, requiring robust measures for prevention and detection. The proposed project, SafeScan, offers a comprehensive solution to address these challenges by developing an integrated system for proactive fraud detection in digital payments. The system employs advanced validation mechanisms to verify QR codes and UPI IDs, ensuring the authenticity of payment details while detecting tampering or spoofing. Concurrently, Machine Learning algorithms are utilized to analyze transaction patterns, including frequency, amount, and user behavior, to identify anomalies indicative of fraudulent activity. SafeScan also features an automated response system that blocks transactions flagged as suspicious and notifies users in real-time, empowering them to take immediate action. Additionally, the system provides an admin dashboard for real-time monitoring ensuring adaptability to emerging fraud tactics. By transmitting transactional and fraud detection data to a centralized platform for visualization and analysis, users can remotely monitor activity and make informed decisions. SafeScan aims to revolutionize digital payment security by offering an intelligent, scalable, and user-friendly solution, thereby fostering trust and confidence in the financial ecosystem.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_35How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - B. Sudha Madhuri
AU  - Jillidimudi Lalitha Vasavi
AU  - Krishna Vineetha
AU  - Patnaik Kuppili
AU  - Munakala Priya
AU  - Panduri Rakshitha Ratna Sai
PY  - 2025
DA  - 2025/11/04
TI  - Safescan: Proactive Fraud Detection In Digital Payments Using Ml
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 394
EP  - 404
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_35
DO  - 10.2991/978-94-6463-858-5_35
ID  - Madhuri2025
ER  -