The challenge
Ignorance of some benefits and services of the compensation fund for members and their families (value creation). It was also crucial for the benefits communication associated with mental health care in the context of the pandemic, in addition to avoiding travel and access to services that were not previously virtual.
The Solution
Implementation of current Flutter technologies, reactive, elastic, resilient architectures, non-relational databases, microservices, using agile work methodologies.
The implementation of new technologies and the commitment to a disruptive and agile framework that impacted their traditional way of approaching projects. The change in the government model was transcendental from the point of view of technology and work culture.
The challenge
Generate greater efficiency in customer service processes by centralizing important information.
Have a single information repository to validate data related to the status of “cesantías” (a saving fund that people can access when their job contract ceases), voluntary and mandatory pensions, all of this, while improving the user experience.
The Solution
We created a solution implemented on a reactive architecture and developed with state-of-the-art technological tools.
We migrate the data contained in multiple sources to a non-relational database. This challenge was overcome by executing the migration iteratively and incrementally, with a rigorous quality and measurement process.
This solution became the centralized and approved source of information for the company, generating: greater efficiency in customer service processes, updating data and sending extracts and certificates, allowing a unified display of the information and reducing operating costs.
The challenge
Guarantee the correct people identification to access the company’s digital channels, to ensure the platform’s reliability and to prevent fraud problems, as could happen in the case of someone who needs to request a loan online.
The Solution
We generated an application based on a prediction model using artificial vision that established a random challenge for the person who was requesting a loan, which allowed us to validate if the person was alive and in turn, prevent fraud through the prediction of the machine model and applied learning.