Measuring the risk of lending to the unbanked population is a challenging task due to the scarcity and quality of the data. By integrating multiple sources seamlessly into a multi-pronged machine learning model ensemble, Aleph CFP has been deployed to provide reliable creditworthiness estimates with up to 85% accuracy.

Traditional product recommendation methods model interactions between sessions, where a main basket of suggestions is generated based on historical data. Using our unique machine reasoning technology, Aleph CFP is capable of modeling interactions within a given session, so that suggestions evolve and adapt during a given visit as the user provides feedback.

Contemporary business models require individualized client modeling. Leveraging its personalization framework and scalable features, Aleph CFP is a key component for differentiated price optimization in loyalty programs, maximizing revenue and consumer value.

Keeping track of changing customer behavior is a necessity in enterprises that depend on satisfying a large pool of consumers. However, most traditional methods are developed for the detection of sudden statistical anomalies with respect to a global pattern, in addition to being ill-equipped for managing a large number of subjects. Powered by its parallel and scalable machine learning methods,  Aleph CFP provides the machinery for detecting, ranking and evaluating relevant patterns in time.