This project aims to develop a financial model that integrates consumer survey data into traditional credit scoring systems, specifically targeting emerging markets where conventional credit data is often sparse. The problem addressed is the limited predictive power of traditional credit scoring methods in these regions. By leveraging Kasi Insights' proprietary survey data, the project seeks to enhance the accuracy of credit risk assessments. The goal is to create a model that combines self-reported financial attitudes and spending intentions with observed credit behavior, thereby improving predictive accuracy and promoting financial inclusion. This project will provide learners with the opportunity to apply their knowledge of financial modeling and data analysis to a real-world problem, demonstrating the practical applications and economic impact of using alternative data sources in credit scoring.

The deliverables for this project include a comprehensive financial model that integrates consumer survey data with traditional credit scoring metrics. Additionally, a detailed report explaining the methodology, findings, and potential implications for financial inclusion in emerging markets will be required. The team will also prepare a presentation to showcase their model and findings, highlighting the enhanced predictive power and practical applications of their approach.