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Aleph Consumer Finance Platform (CFP)

Aleph CFP is a suite of agent-based, personalized solutions for consumer finance and retail applications, capable of integrating arbitrarily large collections of data from multiple, heterogeneous sources, using them as a basis to construct individual models, applicable to a whole variety of scenarios.

Leverage our personalization technologies and boost consumer engagement!

Aleph CFP Use Cases

Personalized

Builds individualized models centered on each particular customer's beliefs and behaviors

Scalable

Easily handles large datasets with different levels for personalization

Deployable

Provides simple and intuitive APIs allowing easy integration with customer and third-party applications

Integrated creditworthiness assessment

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.

Real-time product recommendation

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.

Personalized pricing and revenue optimization

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.

Long-term anomalous behavior modeling

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.