Exploring the Nuts and Bolts of Credit Fund's Risk Transfer Deals: Seeking Insights

Hi community, 

I am currently a junior data analyst at a credit fund, and I'm delving into the intriguing world of Synthetic Risk Transfer (SRT) deals. My goal is to gain a deeper understanding of how the investment process works, particularly when it comes to accessing each obligor/loan's default probability.

In our line of work, we often acquire SRT deals from banks, and I'm curious about the mechanisms behind assessing the default probability for each obligor. If any of you have insights or experiences to share in this area, I'd greatly appreciate your input.

Here are some specific questions I have in mind:

1. Data Sources: How do credit funds typically access data on obligors' default probabilities when acquiring SRT deals from banks? Are there specific data providers or sources commonly used in the industry?

2. Data Enrichment:  What strategies or techniques are employed to enrich the existing dataset? Given that we currently have basic reference data for underlying obligors (e.g., obligor name, maturity, and facility type), how can we enhance this data to gain a more comprehensive understanding of the underlying risks?

3. Predictive Analytics: Are there any best practices for using this enriched dataset to generate insights on obligors and assess risks more effectively? How can predictive analytics be leveraged to estimate potential returns?

I believe that by enhancing our data and analytical capabilities, we can provide better support to our deal team and make more informed investment decisions in the dynamic world of credit risk transfer deals.

Thank you for your time and expertise.