Research Intelligence
550,000+ publications from 700+ African universities, with plain-language summaries, verified researchers and the commercial angle on each piece of work.
Kwame Nkrumah University of Science and Technology · Ghana · Imbalanced Data Classification Techniques
compromising their security and trustworthiness. This research presents an enhanced method for detecting mobile money fraud by modifying a CNN-BiLSTM model with momentum using Stochastic Gradient Descent
Catholic University College of Ghana · Ghana · Imbalanced Data Classification Techniques
robust systems that would effectively detect and if possible prevent these unscrupulous occurrences to a large extent. The features of mobile money transactions dataset are highly unstructured
National Open University of Nigeria · Nigeria · Imbalanced Data Classification Techniques
time monitoring systems are most effective in combating mobile money fraud. The study recommends the adoption of collaborative fraud detection frameworks among mobile money operators and strengthened regulatory
University of Nigeria · Nigeria · Imbalanced Data Classification Techniques
Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature
University of Johannesburg · South Africa · Imbalanced Data Classification Techniques
Experiments were conducted on three benchmark datasets: IEEE-CIS Fraud Detection, European Credit Card Transactions, and PaySim Mobile Money Simulation, each representing diverse transaction behaviors and data distributions
University of Nairobi · Kenya · Cybercrime and Law Enforcement Studies
explore Mobile network fraud in Kenya identifying the most common types of fraud, ways which service providers and regulators are employing to prevent or reduce fraud, methods currently
University of Carthage · Tunisia · Imbalanced Data Classification Techniques
validated on both the Kaggle credit card dataset and the PaySim synthetic mobile money dataset, demonstrating robustness and cross-domain generalizability. These findings highlight the effectiveness of combining
The University of Dodoma · Tanzania · ICT Impact and Policies
security in mobile money services (MMSs) in Tanzania. The IRBAM-2FA combination is novel for Tanzania’s MMS, leveraging unique iris patterns and liveness detection to enhance security
Kwame Nkrumah University of Science and Technology · Ghana
Mobile money services have become essential in providing financial inclusion, particularly in underserved and
University of Johannesburg · South Africa · Imbalanced Data Classification Techniques
that unifies model explainability, statistically valid uncertainty quantification, and operational decision support for fraud detection. ITCF combines instance-level explanations generated via Local Interpretable Model-Agnostic Explanations (LIME
University of Limpopo · South Africa · Imbalanced Data Classification Techniques
financial transactions identifying the most effective individual machine learning (ML) algorithms for fraud detection, developing a hybrid ML model that combines multiple algorithms, evaluating model performance using financial
University of Ngaoundéré · Cameroon
risk factors, and support the development of predictive or preventive strategies against mobile money fraud
Zetech University · Kenya · Internet of Things and AI
cybersecurity threats in Africa, where digital transformation particularly mobile money platforms has exposed vulnerabilities such as SIM-swap fraud, USSD attacks, and identity-driven intrusions. Reported losses, including
Nasarawa State University · Nigeria · Digital and Cyber Forensics
study examines the association between digital forensic capabilities and fraud detection effectiveness in Nigeria’s five largest deposit money banks—United Bank for Africa Plc, Zenith Bank
Nnamdi Azikiwe University · Nigeria · Cybercrime and Law Enforcement Studies
This study examines the relationship between Financial Technology (FinTech), cybercrime, and the performance of
Federal University of Technology Owerri · Nigeria · Financial Distress and Bankruptcy Prediction
billion to fraud in 2024 alone, and automated fraud detection approaches remain largely unexplored specifically within the Nigerian mobile money context. This study presents a comparative evaluation
University of Ngaoundéré · Cameroon
risk factors, and support the development of predictive or preventive strategies against mobile money fraud
Dar es Salaam Institute of Technology · Tanzania · Cybercrime and Law Enforcement Studies
where fraud incidents are often over within hours. This paper reviews published machine learning approaches to insider threat detection, draws a clear operational line between reactive detection
Kwame Nkrumah University of Science and Technology · Ghana
Abstract This study proposes a machine learning framework to enhance mobile money security through fraud detection. Motivated by increasing threats like fraud and unauthorized access, it evaluates Gradient
Mzumbe University · Tanzania
Corruption is a global conundrum that requires multi-faceted approaches to overcome. It is
The University of Dodoma · Tanzania · Cybercrime and Law Enforcement Studies
security in mobile money services (MMSs) in Tanzania. The IRBAM-2FA combination is novel for Tanzania’s MMS, leveraging unique iris patterns and liveness detection to enhance security
Dar es Salaam Institute of Technology · Tanzania
mobile platforms. This research advances Kiswahili NLP, strengthens digital security infrastructure in Tanzania, and offers a scalable solution to combat SMS-based fraud in mobile money ecosystems
University of Benin · Nigeria · Microfinance and Financial Inclusion
The study investigated the link between financial inclusion and economic growth in Nigeria over
Mohammed V University · Morocco · Imbalanced Data Classification Techniques
Abstract Integrating Machine Learning (ML) in medicine has unlocked many opportunities to harness complex
