ItemConference Paper

Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection

Title
Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection
Creator
Singh, Ajay; Sharma, Ambu; Kumar, Pawan; Taneja, Sanjay; Bhatnagar, Mukul
Description
Leveraging the unparalleled adaptability and hierarchical feature stratification capabilities of deep learning, this study constructs a sophisticated framework for fraud detection, seamlessly integrating convolution and recurrent neural architectures with advanced anomaly detection algorithms to decode complex, nonlinear transactional patterns within heterogeneous financial datasets, thereby enabling real-time fraud identification while addressing pivotal challenges of algorithmic interpretability, adversarial resilience, regulatory compliance, scalability, and data confidentiality, ultimately redefining the paradigm of automated financial security in an increasingly digitized global economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Source
Lecture Notes in Networks and Systems;Volume;1464 LNNS;pp.393-403
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial intelligence; Deep learning; Financial security; Machine learning and fraud detection; Neural network; Robust
Coverage
Singh A., Christ (Deemed to be University), Karnataka, Bengaluru, India; Sharma A., Chandigarh University, Chandigarh, India; Kumar P., Graphic Era Deemed to be University, Dehradun, India; Taneja S., Graphic Era Deemed to be University, Dehradun, India; Bhatnagar M., Graphic Era Deemed to be University, Dehradun, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981967519-7;
Format
online
Language
English
Type
Conference paper

Citation

Singh, Ajay; Sharma, Ambu; Kumar, Pawan; Taneja, Sanjay; Bhatnagar, Mukul, “Deep Learning for Uncovering of Fraud: A Design for Automated Financial Protection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25598.

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