ESG AI-powered decision-making platform from the largest bank in the Eurozone.
Santander | Finance
Swift
ASP.NET Core
Docker
AWS ECS
MS SQL
Santander
Santander is one of the world's largest financial groups and plays a significant role in the global sustainability agenda, acting as an agent of change through its credit, investment, and capital allocation decisions.
Their Net Zero program seeks to align operations and portfolios with the Paris Agreement, supporting the decarbonization of clients and emissions-intensive sectors, with decisions increasingly based on ESG data, analytics, and artificial intelligence.
Specialties
Project Management Officer
Tech Lead
UI/UX Designer
Front End Developer
Back End Developer
DevOps
Data Scientist
QA
Challenge
The ESG challenges for Santander involve balancing sustainability, social impact, and robust governance without compromising profitability. This requires reducing its own and its clients' carbon footprint, expanding financial inclusion, and maintaining high standards of transparency and regulatory compliance. For the ESG team, this translates into the operational challenge of collecting, organizing, and interpreting large volumes of heterogeneous and imprecise data, making it essential to automate and normalize information to gain scale, deepen analyses, standardize investment scores, and ensure global reach for the ESG initiative.
Smart Solution
The solution is developed entirely by VX, combining a robust layer of experience, management, and governance with artificial intelligence applied to ESG analysis. VX is responsible for defining the UI/UX and for the complete front-end and back-end development of the application, including authentication, user management, company and source registration, reporting dashboard, publication workflow, and secure storage of PDF and DOC files.
The platform analyzes structured and unstructured sources to generate environmental scores, indicators, and textual reports, as well as identifying controversies when conflicting information exists. These results can be reviewed by users with Editor privileges, always with a recorded justification, ensuring traceability, governance, and scalability to support deeper ESG analyses with global reach.
Our Approach
VX assumed two clear missions upon being hired: in the short term, to stabilize the level of technology service; and, in the medium and long term, to reshape Conduent's technological base. A key point was the commitment to preserving existing operations, requiring close collaboration between the teams of both companies to ensure strategic alignment and achieve the expected results.
Requirements and Workflows
Mapping business objectives, ESG criteria, user profiles, and operational flows, aligning technical, regulatory, and governance expectations from the outset.
Product Discovery and Prototyping
Exploring hypotheses, defining user experience, and prototyping critical interfaces and journeys, ensuring adherence to the ESG team's real processes before development.
Definition of AI Models
Selection and design of artificial intelligence models for ESG data analysis, generation of scores, indicators, textual reports, and identification of controversies.
Solution Development
Implementation of front-end, back-end, integrations, data management and source storage, consolidating the platform into a secure, scalable and auditable architecture.
Testing and Validation
Functional, technical, and analytical validation of the solution, with quality, safety, performance, and regulatory compliance testing, including human review of AI-generated results.
MLOps
Implementation of MLOps practices for versioning, monitoring, and continuous evolution of models, ensuring performance, traceability, and adaptation over time.
