Risk Analysis of SEC 10-K Documents using OpenAI o1 Reasoning Models

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

This case study provides a practical application of EDA techniques in a real business context. Beyond utilizing EDA methods, the goal is to foster a fundamental comprehension of risk analytics within the banking and financial services sector. The focus extends to understanding the role of data in mitigating financial risks associated with lending.

Evaluating the effectiveness of Unichain's MEV framework (maybe mention Rollup-Boost and Flashblocks), quantifying how much MEV is redistributed to users versus captured by others, and comparing these metrics to other L2 solutions.