Fraud Detection

Accurately identify frauds and reduce costs of manual review

More than 90% of online fraud detection systems rely on automated rules to identify fraudulent transactions. Though a quick fix, it leads to to a high rate of false positives. Other still use manual review, which proves both costly and time-consuming. Even with exhaustive training of employees, the process of review often leads to customer frustration.

Business Driver

Identify fraudulent transactions with higher accuracy and lower costs than those associated with the traditional methods of human review and automated screening rules

Data Needed
  • Dataset containing credit card transactions with as many chaacteristics as possible such as transaction type, shipping address, billing address, date and time, amount, credit card number, reference number, actual pin, entered pin, location
  • Probability that a transaction is fraudulent

Methods Used

Wide range of different classifiers

  • Naïve Bayes
  •  K-Nearest Neighbour
  • Support Vector Machines
  • Random Forest
  • Logistic Regression
  • Neural Networks

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InsightOut Analytics

InsightOut Analytics is a consulting firm for Data Science, Machine Learning & AI.

We develop complete business solutions which enable our clients to stay competitive in their industries by taking data-driven decisions.

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+ 40 726 383 872