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Credit Card Fraud Detection

Machine Learning to Detect Fraud

Credit card fraud is a business risk that can have a severe impact not only on the financial institutions that are credit card providers but also on the businesses whose services or products have been procured fraudulently. Time spent investigating a fraudulent transaction, making insurance claims or negotiating with the bank card provider can be better used in running your business. Fraud is an ongoing problem that can cost businesses billions of dollars annually and damage customer trust. Many companies use a rule-based approach to detect fraudulent activity where fraud patterns are defined as rules. Implementing and maintaining rules can be a complex, time-consuming process because fraud is constantly evolving, rules require fraud patterns to be known, and rules can lead to false positives or false negatives.


A solution that can push each card transaction into a data stream for analysis against a machine learning model that has been trained to detect credit card fraudulent transactions. The solution should not require machine learning expertise but can be tweaked to improve accuracy as required.


Photo by Blake Wisz on Unsplash

Machine Learning to the Rescue

Machine learning (ML) can provide a flexible approach to fraud detection. Instead of using pre-defined rules to determine whether activity is fraudulent, ML models are trained to recognize fraud patterns in datasets, and the models are self-learning which enables them to adapt to new, unknown fraud patterns. ML models can extract knowledge from unlabeled data, flagging anomalous transactions for review. Fraud Detection Using Machine Learning solution. This solution automates the detection of potentially fraudulent activity, and flags that activity for review in real time.

Credit card

Photo by Patrick Tomasso on Unsplash

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