Project for the Customer Experience area of ​​a Telco operator. Preparation and quantitative and qualitative analysis of customer surveys to determine the basic indicator of customer experience with the company and its services.

The Bluetab Data Scientist team designed an automatism for the extraction and processing of the sample databases and the subsequent collection, processing and analysis of customer responses. For this, SPSS migrated to R and Python, Spider, database treatment in SQL was used in the first versions to continue with the preparation of the introduction of survey results carried out with AI.

Design of the predictive model of Web Frauds carried out by Bluetab for a leading financial institution in the Spanish market

It consists of developing a predictive algorithm capable of classifying browsing sessions according to their similarity to sessions in which a fraudulent transfer has occurred. The output of this algorithm will be a scoring (or a probability) that will allow ordering these sessions by their probability of being fraudulent.

For this, information from the management of alerts and claims of fraud in Remote Banking Transfers is used, as well as the daily information of the browsing sessions on the web and in the APP; therefore, the target audience or population on which this model will be executed is the one that has started a session by one means or another.

Our methodology is based on the construction of the target based on the crossing between fraud and navigation information (the most critical process of a predictive model), in the diagnosis of inconsistencies among fraudulent operations that do not have an associated session, outliers with some wrong record on date.

As a follow-up to the technological strategy of one of the leading financial institutions, a machine learning algorithm is implemented to predict and prioritize the best offering mix to propose to customers, with the aim of increasing cross-selling and linking of the installed customer base.

By implementing ML, it is possible to enhance the commercial strategy of the entity, broadening the customer vision and integrating with the current CRM, which will increase the probability of success of the actions to be carried out, improving income and customer satisfaction and loyalty. For this, the algorithm prioritizes through existing campaigns crossing with alternative products such as loans, mortgages, funds, cards, pension plans, or different types and insurance coverage (home, health, car)

As output, it is intended to show the improvement by measuring the increase in specific indicators such as: