Social engineering fraud is among the most notorious forms fraud through which people continue to lose money. Its increasing prevalence is negatively affecting strides made in mobile and digital banking. Despite efforts in creating public awareness, its mitigation has not been effective as the tricks used by swindlers keep evolving. Virtually all existing solutions to the problem are based on human interventions such as manually reporting and blacklisting phone numbers. This approach is slow and inefficient due to the huge number of incidents reported relative to the limited existing human resource capacity. This paper presents an evaluation of the feasibility of using classifiers to detect voice-based social engineering fraud. Findings suggest that the use of speaker recognition, speech recognition and classifiers can automate the detection of voice-based social engineering fraud. Outcomes of this research can be used to develop a system that can automatically detect when a criminal is attempting to defraud a user over the phone.
|Keywords||Artificial intelligence, social engineering fraud, voice recognition, classifier, reasoning system|