About Emmanuella Acheampong
Artificial Intelligence, Cybersecurity & Digital Security
Emmanuella Acheampong works at the intersection of artificial intelligence, cybersecurity, and digital
trust. Her work explores how intelligent systems can be used to detect fraud, protect digital identities,
and strengthen modern security infrastructures.
Her interests include machine learning applications, cybersecurity strategy, identity protection, and the
broader impact of emerging technologies on financial and digital ecosystems.
Her work spans research, applied systems, and thought leadership in artificial intelligence and
cybersecurity.
Artificial Intelligence & Machine Learning
Cybersecurity & Digital Identity Protection
Artificial Intelligence
Exploring machine learning systems that detect patterns in complex data and support intelligent
decision-making in digital environments.
Cybersecurity
Research on digital security, cyber threats, and strategies for protecting data, financial systems, and
online infrastructure.
Identity & Fraud Detection
Investigating identity theft, fraud detection models, and the role of artificial intelligence in preventing
financial crime.
Research & Projects
Financial Loss Severity in Bank Identity Theft
An empirical study examining how different types of bank-related identity theft influence financial losses
experienced by victims.
Secure-by-Design Escalation Framework for Fraud Detection
A cumulative risk scoring framework designed to detect account takeover fraud by analyzing
authentication and transaction events.
Human-in-the-Loop Cybersecurity Decision Framework
Examining the role of human expertise in machine-learning-driven cybersecurity systems and fraud
detection.
Speaking & Conference Presentations
Selected conference presentations, invited talks, and research discussions on cybersecurity, fraud detection, and data analytics.
Blog & Insights
Short reflections and insights on cybersecurity, fraud detection, artificial intelligence, and data analytics.
Human-in-the-Loop Cybersecurity
While AI can detect suspicious behavior at scale, human expertise remains essential for interpretingalerts and making critical decisions in cybersecurity operations.
Machine Learning for Fraud Detection
Machine learning models can identify hidden patterns in banking data that traditional rule-basedsystems often miss. This article explores how predictive analytics can support real-
Understanding Identity Theft in Digital Banking
Identity theft continues to rise as digital banking expands. This article discusses how fraudsters exploitauthentication systems and how data-driven detection methods can help fina
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