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Friday, March 2, 2018

Machine Learning Use Cases in the Financial Services

With the rapid changing digital age and our more dependence on the digital services in every domain from banking, payments, medical, ecommerce, investments etc needs a technology delivery model that's suited to how the world and consumer needs are changing so as to allow companies to develop new products and capabilities that kind of fits with the digital age.

We are considering the Capital One's use case for the Machine learning

There were 3 main fields for the Capital one to improve there banking relationship - Fraud Detection, Credit Risk, Cybersecurity. Improving these areas involves distinguishing patterns, something the neural networks underlying machine learning accomplish far better than traditional , non-AI software. The goals were to Approve as many transactions as possible by identifying fraud only when it's very likely to happen; make better decisions making around credit risk, track constantly evolving threats. Applying machine learning to these areas is a big oppurtunity.

One recent innovation for the same was a tool called SECOND LOOK , which uses machine learning to alert customers about unusual spending patterns, such as double charges, a repeating charge that is higher than the previous month , or a tip that's higher than the norm. According to the company , Second look saved customers millions of dollars in unwanted charges. 

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