Sentinel Predictions is a visual design environment to build predictive analysis models for fraud prevention, money laundering, risk analysis, and customers of behaviors, among others. It provides a complete library of learning algorithms, data set-up and exploration, model validation tools, and the model assessment server integrated with Sentinel.
Sentinel Predictions models generate a score that can be used by the Sentinel transactional engines, both in a "real time" approach and "near real time", combined with the different analytical tools and decision-making process already provided by the system.
Data exploration through a series of statistical techniques makes it possible to understand the data composition, the generation of different groups and behaviors of the subjects in analysis: cardholders, channels, customers, transactions, branches, ATM’s, etc.
In many cases, the generation of predictive models requires data set-up, because it does not necessarily have the optimum quality, it has incomplete values, it requires screening and mix of different information groups, or the generation of new data from the existing data:
Sentinel Predictions has a broad variety of supervised and non-supervised learning algorithms for the generation of models. The use of each algorithm usually depends on what is to be predicted, as well as on the data quality and quantity. On many occasions for the same objective, for example, fraud prevention, multiple models are generated through different algorithms so that they compete to obtain the best results and ultimately have a ‘champion’.
Estimating the model performance and its accuracy is essential to determine if it is possible to set it up in production, assessing the online information supplied by Sentinel or if any fine tuning is required.