Best ChatGPT prompts for Credit Analysts

Given a Snowflake database with 500 million rows of customer credit data, a Python script using the Pandas library to handle data transformations, and a target runtime of under 2 hours, identify the top 3 bottlenecks in the current ETL pipeline and provide a revised version of the pipeline that utilizes SQL queries to reduce data processing time, including a step-by-step breakdown of the changes made and the expected performance improvements.

Using a dataset of 10,000 customer loan applications, each with 20 features such as credit score, income, and loan amount, generate a comprehensive statistical summary of the data, including mean, median, mode, and standard deviation for each feature, as well as a correlation matrix to identify relationships between features, and a list of the top 5 most influential factors in determining credit risk, based on a regression model built using R and the caret package.

Given a trained regression model built using Python and Scikit-learn to predict credit default probabilities, with a current model precision of 80%, identify the top 3 factors contributing to model error, and provide a refined version of the model that incorporates additional features such as macroeconomic indicators and customer demographic data, including a comparison of the original and refined models' performance on a holdout test set, and a discussion of the potential risks and benefits of incorporating these new features.

Using a dataset of 1 million customer credit reports, each with 50 fields such as account balances, payment histories, and credit inquiries, develop a data cleaning script in SQL to identify and correct errors in the data, such as missing or duplicate values, and generate a report detailing the number and type of errors found, as well as a summary of the data quality metrics before and after cleaning, including a discussion of the potential impact of these errors on downstream credit scoring models, and recommendations for implementing data validation checks to prevent similar errors in the future.