Fraud in African financial and digital ecosystems takes many forms: account takeover, synthetic identities, merchant collusion, social engineering, and more. As digital channels grow, so do the opportunities for abuse.
Fraud analytics uses patterns in transaction data, device fingerprints, behavioural signals and external intelligence to detect suspicious activity. Models can flag anomalies in real time, enabling quicker intervention.
However, fraudsters adapt. Analytics teams need to monitor performance, refresh models, and incorporate new data sources. Collaboration and data sharingâwithin regulatory boundariesâcan strengthen defences across the ecosystem.
Ultimately, fraud prevention is a continuous race. Organisations that combine analytics, strong controls and customer education will be better placed to protect both their finances and their reputations.