30 Intelligent Financial Product Recommendation Engine PFE

Project overview

• Develop an AI module integrated into an existing banking solution to provide personalized financial product recommendations (savings, investments, loans, insurance).
• The system analyzes transactions, spending habits, financial history and demographic data to generate proactive, relevant and personalized recommendations while ensuring privacy and compliance.

Responsibilities & scope

• Design and implement data pipelines to aggregate and preprocess client transaction, behavior and profile data for model consumption.
• Build and train recommendation models or rule-based hybrid engines to score and rank appropriate financial products for each client.
• Integrate the AI module with the existing backend (Java / Spring Boot) and expose APIs for the web/mobile client stack (React JS / React Native).
• Implement intelligent notifications and delivery mechanisms via the client interface (web and mobile).

Technologies & required profile

• Required technologies: React JS / React Native for client integration, Java / Spring Boot for backend services, PostgreSQL for data storage.
• Engineer profile expected; project open for 1 trainee (PFE) to join and work under supervision of the engineering team.
• Strong knowledge expected in data analysis, machine learning / recommender systems, REST APIs and relational databases.

Security, compliance & quality

• Ensure recommendations respect user privacy and comply with banking regulations and internal policies.
• Implement data handling safeguards, anonymization where required, and logging/auditing for compliance review.
• Deliver unit and integration tests for the module and provide deployment and rollback procedures.

Deliverables & outcomes

• A deployed AI recommendation module integrated into the bank’s solution with documented APIs consumed by web/mobile clients.
• Source code, model artifacts, test suites, and technical documentation (integration guide, data schema, compliance notes).
• A final project report and demo showing end-to-end flow: data ingestion → scoring → recommendation delivery via client interfaces.

How to apply

• Apply via the trainees platform: https://trainees-platform.proxym-group.net
• Use the email subject line when contacting HR or referencing the project: "PRX-2026-15 - Application - Intelligent Financial Product Recommendation Engine PFE"