Machine Learning and Fraud: Why Artificial Intelligence Isn't Enough - Dataconomy

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. 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.