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Gbeminiyi Ogunleye
3 months ago
Overview
Decoding Innovation: How Dr. Faith Ugbeshe is Redefining Online Banking Fraud Detection with Machine Learning and Artificial Intelligence
In a world increasingly driven by technology, tackling the ever-evolving threat of fraud in online banking requires a blend of innovation, expertise, and determination. For Dr. Faith Ugbeshe, PhD, this challenge was an opportunity to make a transformative impact.
Dr. Faith recently completed her PhD in Computer Science, specialising in Machine Learning and Artificial Intelligence, with a research focus on developing a machine learning-based anomaly detection model for online banking fraud. Her work addresses one of the most pressing concerns for modern financial systems: fraud detection.
Existing fraud detection systems, struggle with a significant issue: balancing accuracy with efficiency. False positives, which involve flagging legitimate transactions as fraud, and false negatives, which involve missing actual fraudulent activities, plague these systems, causing disruptions for customers and financial losses for institutions.
Recognising this challenge, Dr. Faith embarked on a mission to design a solution that not only reduces these errors but also adapts to the evolving nature of fraud tactics.
Her innovative approach leverages behavioural analytics to enhance the detection process. By analysing transaction patterns, user behaviour, and contextual data, her model identifies anomalies that might indicate fraud while minimising disruptions to legitimate users. This sophisticated approach ensures a higher level of precision, enabling banks to detect fraudulent activities in real time without overwhelming their systems with unnecessary alerts.
