Artificial intelligence based fake or fraud phone calls detection

Artificial intelligence based fake or fraud phone calls detection

Registration ID: IJNRD_224556

Published ID: IJNRD2407033

DOI: Click Here to Get

Avula poojitha , P Satish Kumar , Mannuru malleswari

Artificial intelligence, fake or fraud phone calls detection, NLP

Abstract The advent of telecommunications has revolutionized global connectivity, enabling rapid and efficient communication across diverse populations. However, this digital transformation has also led to a rise in fraudulent activities, particularly through deceptive phone calls. These fraudulent calls, orchestrated by sophisticated criminals, aim to exploit vulnerabilities in human trust and technological systems to deceive individuals into divulging sensitive information or participating in illicit financial transactions. Detecting and preventing such fraudulent activities presents significant challenges due to the dynamic nature of fraud tactics and the real-time demands of voice communication. This study explores the application of artificial intelligence (AI) techniques to develop robust systems for detecting and mitigating fake or fraud phone calls, thereby enhancing security in telecommunications networks. The research focuses on leveraging advanced machine learning algorithms, natural language processing (NLP) techniques, and voice analysis technologies to analyze call patterns, voice characteristics, and contextual information in real-time. By extracting meaningful features from phone call data, including audio recordings and call transcripts, AI models can discern anomalous behaviors and identify suspicious calls indicative of fraud. Key methodologies include the acquisition and preprocessing of a diverse dataset of phone call recordings, encompassing labeled examples of genuine and fraudulent calls. Privacy considerations and ethical guidelines govern the collection and anonymization of sensitive call data to protect user confidentiality while ensuring dataset integrity. Feature extraction techniques, such as spectrogram analysis for voice signal processing and NLP for linguistic pattern recognition, contribute to the development of accurate and reliable fraud detection models. The effectiveness of AI-based fraud detection systems is evaluated through rigorous model training, validation, and optimization processes. Supervised learning algorithms, including Support Vector Machines (SVM) and deep neural networks, classify calls based on extracted features and historical fraud patterns. Unsupervised learning techniques, such as anomaly detection and clustering, uncover unusual call behaviors without prior labels, enhancing detection capabilities across diverse fraud scenarios. In conclusion, the integration of artificial intelligence into fraud detection systems for identifying fake or fraud phone calls represents a significant advancement in safeguarding telecommunications infrastructure. By enhancing detection accuracy, real-time response capabilities.

Published Paper   E-Certificate

"Artificial intelligence based fake or fraud phone calls detection", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.a319-a328, July-2024, Available :https://ijnrd.org/papers/IJNRD2407033.pdf

Volume 9 Issue 7, July-2024

Pages : a319-a328

Paper Reg. ID: IJNRD_224556

Published Paper Id: IJNRD2407033

Downloads: 000121200

Research Area: Computer Engineering 

Country: Cuddapah, Andra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407033.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407033

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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