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9 IT projects primed for machine learning

3. Fraud detection

Spotting fraudulent and anomalous transactions is a classic data analytics problem, but if you’re doing it at a large scale, machine learning helps spot problematic activity such as scammers making multiple payments just under a trigger limit, new merchants exhibiting unusual behaviour and apparently legitimate customers who are connected to a network of scammers. Fraud.net uses Amazon Machine Learning to train multiple machine learning models to spot a range of fraudulent activity rather than trying to create a single model to score every possible kind of fraud; on any given day the merchants they protect might be facing a hundred different fraud schemes, each with dozens of variations.

Machine learning isn’t just useful for catching fraud by existing customers — insurers want to spot new applicants who plan to claim for a car that’s already been damaged before they issue a policy. And don’t just think about blocking bad transactions. In the US, Ford’s credit division is using machine learning tools from ZestFinance to predict the likelihood of specific borrowers paying back a loan so it can lend to people with lower credit scores. With car sales in the US falling generally (and a slightly larger decline for Ford itself), finding buyers they’d otherwise turn down could be a big help to the business. Machine learning can help you tell good customers from bad risks more quickly.

4. ERP inventory planning

Supply chain automation isn't new, but machine learning is making it much more common. Instead of just historic sales data, machine learning lets you use data about the way customers research purchases on line, the impact of weather on shopping habits and other internal and external trends to manage inventory by forecasting demand. Amazon claims it can predict exactly how many shirts of a particular color and size it will sell every day; Target credits machine learning predictive models with 15-30% growth in revenue. Online German retailer Otto uses machine learning to predict what will sell in the next 30 days with 90% accuracy, reducing the amount of surplus stock by a fifth and lowering returns by more than 2 million products a year; the automated purchasing system orders 200,000 items a month from third-party suppliers, choosing the colors and styles that are predicted to sell.



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