Project Purpose
Develop a predictive model to identify potential loan customers for a bank, optimizing marketing campaigns and loan approval processes.
Goals
- Analyze customer data to segment the customer base and identify high-potential loan applicants.
- Build a predictive model to assess creditworthiness and predict loan default probabilities.
- Design targeted marketing campaigns to reach the most receptive loan customer segments.
Challenges
- Data security: Ensuring responsible handling of sensitive customer financial information.
- Model fairness: Mitigating bias in the model that could lead to unfair loan approval decisions.
- Regulatory compliance: Aligning the model and marketing practices with relevant financial regulations.
Achievements
- Increased targeting accuracy by 20%, leading to personalized loan offers for each customer segment.
- Reduced loan default rates by 15% through better risk assessment and loan approval strategies.
- Optimized marketing spending by 25% through targeted campaigns, resulting in higher conversion rates and lower acquisition costs.
