DEVELOPMENT OF A CHATBOT CYBERCRIME REPORTING SYSTEM
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Artificial Intelligence, Digital Forensics, Incident Response, Online Fraud
Abstract
The issue of cybercrime in Nigeria is of great concern, as the nation is ranked number five in the world and encountered more than 12.9 million attacks in the 2023 general elections. The old system of cybercrime reporting is still inefficient with a completion rate of 45-60% and 15-30 minutes per report. Although chatbot reporting systems, which are AI-based (in India and Singapore) have enhanced accessibility and interaction with citizens, similar solutions based on the specifics of the socio-cultural, linguistic, and technological environment of Nigeria have not had many opportunities to emerge. The current platforms are mostly based on the use of static web forms with little user instructions provided, and do not have real-time feedback, built-in evidence management, and proper case tracking. This paper introduces the creation of a Chatbot Cybercrime Reporting System (CCRS), which uses conversational AI to simplify the reporting for victims of online fraud and other cyber threats. Implementation was done on a Python Flask backend, PostgreSQL database, and a responsive frontend interface and tested with unit testing, integration testing and user acceptance testing (15 participants), six-week performance monitoring and security audits. Findings show significant increases compared to conventional solutions, such as a report completion rate of 94.2% and an average reporting time of 2.1 minutes, 99.5% operational uptime, and satisfaction with the chatbot user interface (4.6/5), with 93% of respondents choosing to use the chatbot user interface. There were also 150 simultaneous users that the system was handling with no security weaknesses reported. Incorporating more languages, covering a wider range of crime types, incorporating sophisticated natural language processing, and linking with law enforcement databases to provide a more comprehensive approach to cybersecurity nationally could become the focus of future research.
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Authors
Copyright (c) 2026 Azeez Olawale Akinlolu, Ebunayo Rachael Jimoh, Adesoji Henry Adeshina, Abubakar Oladimeji Abubakar, and Mawada M. Elkhalifa
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