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:
Intelligible.
A Decision System composed of 7 gradual rules without chaining, each using some of the 9 variables that XTRACTIS identified as significant (out of 29 potential predictors characterizing each transaction).
Very good Real Performance on External Test.
Efficient & Operational.
Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).
Use Case 2023/06 (v1.0)
Results by
XTRACTIS® GENERATE 12.2.45294 (2023/06)
DOCUMENT CONTENTS
- Problem Definition
- Xtractis Solution
- Top-Model Induction
- Explained Predictions for 2 cases
- Top-Models Benchmark
