XTRACTIS FOR fraud detection
Detection of Fraudulent Credit Card Transactions
Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks
Design, from only some transactions characteristics, an AI-based decision system that efficiently diagnoses credit card transactions, in order to rationally and instantly detect the fraudulent ones and to eventually adapt protection measures.
Identify the parameters involved in the fraudulent transactions and enhance knowledge by helping banking specialists understand the causal relationships between these parameters, their combination, and the occurrence of fraud (i.e., understand the scammers’ strategies).
Help the banking sector to make transparent decisions through automatic, explainable detection, while improving the consumer experience.
Use a detection model with fewer transaction characteristics to speed up protection process.
We get a Predictive Model that is:
LoR=Logistic Regression | RFo=Random Forests | BT=Boosted Trees | NN=Neural Networks
Detailed results and explanations in full document.