Public Use Cases in Business & Finance

Our Use Cases present the results of XTRACTIS modeling and include complete Benchmarks against Neural Networks, Boosted Trees, Random Forests, and Logistic Regression.

These studies illustrate the ability of XTRACTIS to automatically induce knowledge in the form of predictive and intelligible mathematical relationships from real-world data (public data or authorized private data).

For each application, we show how the induced décision system uses its fuzzy rules to compute explained predictions for new situations, i.e.  unknown to the learning data set.

XTRACTIS discovers the strategies to evaluate customer attrition and to assign a churning score to a customer in a rational way in order to adapt anti-churning strategies.

UC#22 - Release: Feb. 2024

XTRACTIS discovers the strategy behind credit card scams based on a few characteristics of each transaction, to instantly detect the fraudulent ones and to eventually adapt protection measures.

UC#20 - Release: Nov. 2023 | Update: Feb. 2024

XTRACTIS robots explore a 27-dimension decision space of possible supply chain configurations to find the one that maximizes at best Operating Income given requests on local sales objectives, local production constraints, and ecological requirements. Objectives and constraints are either fuzzy (flexible) or binary (rigid).

UC#19 - Release: Sep. 2023