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Fraud Detection

Fraud Detection

Problem/Challenge – Fraud is a huge problem in the financial industry. There is fraud in almost every agency serving our nation’s citizens, from mortgage fraud to tax evasion. Nevertheless, experts predict online credit card fraud to soar to a whopping $40 billion by 2021.Detecting and preventing fraud is a huge challenge for banks given the large variety of fraud types and the volume of transactions that need to be reviewed.

Resolution – Machine learning algorithms are able to detect and recognize thousands of patterns on a user’s purchasing journey instead of the few captured by creating rules. Fraud detection process using machine learning starts with gathering and segmenting the data. Then machine learning model is fed with training sets to predict the probability of fraud which would therein enable federal agencies to be more responsive and save billions of dollars to taxpayers.

Why CafeEDA – CafeEDA’s aim is to leverage AI in accurately predicting fraudulent behavior, it will automatically engineer and identify the individual reasons for why each fraud will occur. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeEDA includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.