Telecommunication fraud detection by analyzing the content of a Bangla phone call

Abstract

Telecommunication fraud, especially through techniques like phishing calls, poses a significant threat, resulting in substantial financial losses and compromising sensitive personal data. While technological advancements have made life easier for consumers, they have also introduced new vulnerabilities. Phishing through phone calls, wherein individuals are tricked into revealing sensitive information, is a prevalent method employed by fraudsters. Traditional approaches use blacklists of known fraudulent numbers to detect phishing calls that are ineffective against new or previously unseen numbers. To tackle the problem, I propose a system leveraging conversation transcript analysis, combining an Automatic Speech Recognition (ASR) model to convert audio to text and Natural Language Processing (NLP) techniques to analyze the text. Two datasets are used in my experiment: a transcription dataset generated by a Bangla ASR system and a clean conversational dataset. I demonstrate that fine-tuning deep learning models to increase accuracy works well on transcription data provided by ASR. The study utilizes four deep learning models: Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM). The results demonstrate that the RNN model attains the best accuracy of 97% on the clean conversational dataset. In the transcription dataset, both LSTM and ANN models achieve highest accuracy of 83%. The results emphasize the capability of optimized deep learning models to improve the identification of fraudulent phone calls, especially in situations where the data is provided by ASR system. This provides a strong and effective approach to detect telecommunication fraud.