Fraud is here to stay, but AI can win the war – Analyst

As digital fraud grows increasingly sophisticated, banks are turning to artificial intelligence (AI) not only to detect financial crimes but also to predict and prevent them. Fraud Analyst and Financial Crime Specialist, Joy Ladegbaye, in her latest report, noted that the fight against fraud has moved beyond simply reacting to threats after they occur. Instead, […]

As digital fraud grows increasingly sophisticated, banks are turning to artificial intelligence (AI) not only to detect financial crimes but also to predict and prevent them.

Fraud Analyst and Financial Crime Specialist, Joy Ladegbaye, in her latest report, noted that the fight against fraud has moved beyond simply reacting to threats after they occur. Instead, she argued, the future lies in building intelligent systems that can anticipate criminal activity.

“With well-designed AI systems built on solid data, advanced models, continuous monitoring, and human oversight, banks can do more than react,” Ladegbaye said. “Fraud will never disappear, and fraudsters will never stop innovating, but AI gives us the chance to get ahead.”

She explained that as fraudsters adopt more advanced tactics—including bots, AI, and deepfakes—financial institutions must leverage AI not just to catch fraudulent activity, but to predict and prevent it.

According to her, this requires systems grounded in strong data sources such as real-time transaction streams, device information, and behavioural signals. Tools like Apache Kafka and Flink, she noted, enable banks to process these data streams instantly.

Ladegbaye further stressed the importance of deploying models that are fast, accurate, and explainable. Techniques such as supervised learning, unsupervised anomaly detection, and graph-based models are critical, she said, while hybrid systems that combine AI with rule-based frameworks offer flexibility and help maintain regulatory compliance.

Success in the fight against fraud, however, is not only about detecting more cases. Ladegbaye highlighted the need for balance, pointing to key performance metrics such as precision, false positive rates, and latency as essential in protecting both banks and customers.

“With the right AI systems, banks can move from defence to offence,” she concluded. “This isn’t just about keeping pace with fraudsters—it’s about staying ahead.”