How to Ask Chatbots the Right Questions About Deals

How to Ask Chatbots the Right Questions About CMBS and ABS Deals

Chatbots like ChatGPT have transformed the way we access and analyze data. When it comes to complex financial datasets—such as those from CMBS (Commercial Mortgage-Backed Securities) and ABS (Asset-Backed Securities) deals—asking the right questions can mean the difference between actionable information and frustrating dead ends. Here’s how to maximize your chatbot queries to make sense of these intricate data sets.

1. Understand the Metadata Behind CMBS and ABS Data

Before asking a chatbot anything, it’s crucial to know what metadata underpins the data you’re trying to understand. In CMBS and ABS deals, key metadata includes:

Deal identifiers: Names, ticker symbols, or CUSIPs.

Collateral details: Types (e.g., multifamily, auto loans), amounts, and distributions.

Lifecycle stages: Preliminary prospectus, final prospectus, active surveillance, or matured deals.

Performance metrics: Delinquencies, interest rate distributions, and default rates.

By framing your questions around this metadata, you provide the chatbot with enough context to deliver precise and relevant answers.

2. Ask Specific and Contextual Questions

Chatbots perform best when your questions are clear and specific. Avoid broad or vague queries like "What’s happening in CMBS?" Instead, focus on:

Drill-Down Questions:

"What is the collateral distribution for CMBS Deal XYZ?"

"Show me delinquency rates for ABS auto loan deals in Q4 2024."

Comparative Questions:

"How does the interest rate distribution for Deal ABC compare to the CMBS dataset average?"

"Which ABS deals had the lowest delinquency rates in 2023?"

Trend Analysis Questions:

"What are the delinquency trends for multifamily collateral in CMBS deals over the past 12 months?"

"How have ABS auto loan defaults changed since 2022?"

3. Leverage Filters and Parameters

Most structured finance datasets are vast, so it’s helpful to narrow down the scope using filters. For example:

Timeframe: "Provide data for CMBS deals issued between January and June 2024."

Deal Type: "List all ABS deals focused on auto loans."

Performance Metrics: "What’s the average collateral loss rate for CMBS deals under surveillance?"

Adding parameters ensures that chatbots deliver concise and relevant results instead of overwhelming you with extraneous information.

4. Utilize Natural Language and Keywords

While chatbots can handle natural language queries, integrating specific finance-related keywords improves their understanding. For CMBS and ABS deals, useful keywords include:

"collateral pool"

"credit enhancement"

"prepayment risk"

"tranche performance"

"surveillance report"

For example, instead of saying, "Tell me about this deal," try "What are the credit enhancements for CMBS Deal XYZ?"

5. Ask Follow-Up Questions

Chatbots thrive on iterative conversations. If the initial response isn’t detailed enough, refine your question based on the output. For instance:

Initial Question: "What is the delinquency rate for CMBS Deal ABC?"

Follow-Up: "Break down the delinquency rate by property type for Deal ABC."

Follow-Up: "How does this rate compare to the average in 2024?"

This step-by-step approach can unlock deeper insights from even the most complex datasets.

6. Check for Source Transparency

When dealing with financial data, knowing the data’s origin is crucial. Chatbots often derive responses from a mix of sources. You can ask:

"What is the source of this data?"

"Are the metrics based on preliminary or finalized prospectus data?"

"Is this information from surveillance reports or external analyses?"

Understanding the context ensures you’re working with reliable and actionable data.

7. Use Chatbots to Cross-Reference Insights

Chatbots can act as a powerful tool to validate data. For instance:

Compare delinquency rates from different sources: "Does Dealcharts.org’s delinquency data match the average rates from other CMBS providers?"

Validate metadata consistency: "Are the collateral pool details for Deal XYZ consistent across filings?"

Cross-referencing prevents errors and strengthens your analysis.

8. Save Queries and Automate Reports

For frequently asked questions, consider saving your chatbot queries for reuse. Many chatbots support automations and scheduled reports, enabling you to:

Monitor changes in delinquency rates weekly.

Track interest rate distributions for ABS deals monthly.

Receive alerts for significant changes in CMBS surveillance metrics.

Final Thoughts

Asking the right questions is both an art and a science, especially in the structured finance domain. By understanding the metadata, framing precise queries, and iterating based on responses, you can unlock the full potential of chatbots to analyze CMBS and ABS deals. Tools like Dealcharts.org, combined with a metadata-first approach, ensure that the answers you receive are not just accurate but also actionable.