AI personalization and ethics in banking - College project
Budget: €250 – €750 EUR
I am looking for a skilled and ethical AI/ML Engineer or Data Scientist with experience in financial personalization systems to build an AI-powered banking recommender engine. The ideal candidate should have:
Technical Skills
Strong Python skills with expertise in machine learning (scikit-learn, XGBoost, etc.)
Experience with explainable AI tools (e.g., SHAP)
Familiarity with fairness and bias auditing tools (e.g., IBM AIF360)
Knowledge of recommender systems and customer segmentation
Proficiency in handling synthetic or privacy-safe datasets
Experience with model deployment (basic cloud setup, Streamlit optional)
Domain Knowledge
Understanding of fintech or banking personalization systems
Knowledge of ethical AI principles, bias mitigation, and model explainability
Experience in customer journey mapping or behavioral finance is a plus
Project Description: AI-Driven Financial Product Recommendation System with Ethical Auditing
Overview:
I am developing a smart banking prototype that uses machine learning to recommend financial products (e.g., savings plans, loans, credit cards) based on user behavior like spending, saving, and preferences. The goal is not just personalization, but ethical and transparent AI that builds trust with customers.
Project Scope:
Data Preparation
Use or generate realistic synthetic banking data (demographics, transactions, financial profiles)
Ensure privacy and anonymity
Customer Segmentation
Implement clustering (e.g., K-Means or PCA + K-Means) to identify customer groups based on behavior
Recommender System
Develop a rule-based or hybrid model for personalized product suggestions
Model Explainability
Integrate SHAP to explain model predictions to users
Fairness Auditing
Use AIF360 to detect and mitigate bias across sensitive attributes (e.g., gender, income level)
Evaluation & Ethics
Test system for transparency, fairness, and usability
Provide summary dashboards showing fairness metrics and explanations
(Optional) Web UI / Prototype
A simple UI (Streamlit or lightweight Flask app) to demonstrate model use
Deliverables:
Complete codebase with documentation
Working recommender engine with explainability and fairness integration
Sample demo with test data and outputs
Report/Notebook explaining decisions, logic, and metrics
Timeline:
4–6 weeks with milestone-based payments and weekly check-ins
Technical Skills
Strong Python skills with expertise in machine learning (scikit-learn, XGBoost, etc.)
Experience with explainable AI tools (e.g., SHAP)
Familiarity with fairness and bias auditing tools (e.g., IBM AIF360)
Knowledge of recommender systems and customer segmentation
Proficiency in handling synthetic or privacy-safe datasets
Experience with model deployment (basic cloud setup, Streamlit optional)
Domain Knowledge
Understanding of fintech or banking personalization systems
Knowledge of ethical AI principles, bias mitigation, and model explainability
Experience in customer journey mapping or behavioral finance is a plus
Project Description: AI-Driven Financial Product Recommendation System with Ethical Auditing
Overview:
I am developing a smart banking prototype that uses machine learning to recommend financial products (e.g., savings plans, loans, credit cards) based on user behavior like spending, saving, and preferences. The goal is not just personalization, but ethical and transparent AI that builds trust with customers.
Project Scope:
Data Preparation
Use or generate realistic synthetic banking data (demographics, transactions, financial profiles)
Ensure privacy and anonymity
Customer Segmentation
Implement clustering (e.g., K-Means or PCA + K-Means) to identify customer groups based on behavior
Recommender System
Develop a rule-based or hybrid model for personalized product suggestions
Model Explainability
Integrate SHAP to explain model predictions to users
Fairness Auditing
Use AIF360 to detect and mitigate bias across sensitive attributes (e.g., gender, income level)
Evaluation & Ethics
Test system for transparency, fairness, and usability
Provide summary dashboards showing fairness metrics and explanations
(Optional) Web UI / Prototype
A simple UI (Streamlit or lightweight Flask app) to demonstrate model use
Deliverables:
Complete codebase with documentation
Working recommender engine with explainability and fairness integration
Sample demo with test data and outputs
Report/Notebook explaining decisions, logic, and metrics
Timeline:
4–6 weeks with milestone-based payments and weekly check-ins