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Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets , machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There’s no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run.

Rafael Lourenco

By By Rafael Lourenco

Understanding the e-commerce Payment Chain

Machine Learning and fraud: why artificial intelligence isn’t enough

Machine Learning and fraud: why artificial intelligence isn’t enough

That’s because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results.


Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad — some transactions will be falsely flagged for decline. The costs associated with false declines are too high to ignore. US merchants lose much more money on false declines than on confirmed fraud — $118 billion in false declines, compared to $9 billion in actual fraud, according to MasterCard and Javelin research.

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