{"id":7484,"date":"2020-02-04T19:39:25","date_gmt":"2020-02-04T18:39:25","guid":{"rendered":"https:\/\/exmachina.ch\/?p=7484"},"modified":"2023-06-28T19:17:41","modified_gmt":"2023-06-28T18:17:41","slug":"how-ai-can-support-business-intelligence-fraud-detection-example","status":"publish","type":"post","link":"https:\/\/exmachina.ch\/en\/tech\/how-ai-can-support-business-intelligence-fraud-detection-example\/","title":{"rendered":"How AI Can Support Business Intelligence"},"content":{"rendered":"
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Introduction<\/h3>\n

Nowadays, AI is rapidly transforming different industries thanks to high data volumes, new advanced algorithms, large data storages, and cloud computing. Business Intelligence is not an exception in this case. BI is one of the top industries where injection an AI solution to a problem can bring immense impact. Small to large companies are using this technology to improve efficiency of business processes and their customer’s experience. AI-powered BI solutions can pull the insights from large datasets, build recommendations based on this data and therefore shape the Business Intelligence decision-making.<\/p>\n

For example, recommender systems can suggest a better product to a customer based on his preferences, regression models can predict prices that will find a trade-off between business profit and customer’s paying ability. In this article, we will explore a real-life classification problem of fraud detection.<\/p>\n<\/div>\n

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Loading the dataset<\/h3>\n

For this article we will use Credit Card Fraud Detection Dataset<\/a>.<\/p>\n

The goal of this dataset is to predict whether the credit card transaction is fraud. It is important for companies to detect and reject those transactions in real-time. At first, let’s import some libraries that we will be using and load the dataset.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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In\u00a0[1]:<\/div>\n
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import<\/span> numpy<\/span> as<\/span> np<\/span>\r\nimport<\/span> scipy<\/span> as<\/span> sp<\/span>\r\nimport<\/span> pandas<\/span> as<\/span> pd<\/span>\r\nimport<\/span> matplotlib<\/span> as<\/span> mpl<\/span>\r\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\r\nimport<\/span> seaborn<\/span> as<\/span> sns<\/span>\r\nfrom<\/span> sklearn.manifold<\/span> import<\/span> TSNE<\/span>\r\nfrom<\/span> sklearn.preprocessing<\/span> import<\/span> StandardScaler<\/span>\r\n\r\n# Plotting options<\/span>\r\n%<\/span>matplotlib<\/span> inline\r\nsns<\/span>.<\/span>set<\/span>(<\/span>style<\/span>=<\/span>'whitegrid'<\/span>)<\/span>\r\n\r\ntransactions<\/span> =<\/span> pd<\/span>.<\/span>read_csv<\/span>(<\/span>'..\/input\/creditcard.csv'<\/span>)<\/span>\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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As you can see below, there are only numerical features in the dataset:<\/p>\n