Webclass 141: Creating an AI app for auditors with python and Streamlit

Q1. Is it possible to automatically review contract documents for specific clauses with python?

Q2. Is it possible to scan emails for key words or phrases that may indicate corruption with python?

Q3. Is it possible to create a chatbot with python?

Q4. Is it possible to get ChatGPT to write python code for you without sending your data to ChatGPT?

Q5. Is it possible to predict anomalous transactions with python?

Q6. Is it possible to predict future sales based on an input set of data with python?

Q7. What benefits does Streamlit bring to the internal audit team?

Q8. How much does Streamlit cost?

Q9. What is the command in the CMD line that we have to run to see our streamlit application in a web-browser

Q10. How can we easily open a CMD window in the correct directory where our python program is found?

Q11. Do you need team-members that know about HTML and JavaScript in order to create a web-application for your python results?

Q12. Can you easily have user feedback and interaction with the Streamlit application?

Q13. Can you use Steamlit to connect to an SQL database?

Q14. If you want all of your auditors to be able to use the Streamlit application, where would you store it?

Q15. When using Streamlit, does your data have to go in the cloud?

Q16. Which Artificial Intelligence model did we use to identify anomalies?

Q17. Which Artificial Intelligence model did we use to create a model?

Q18. Which Artificial Intelligence model did we use to use the model to predict anomalies in the following month’s data set?

Q19. Which python library did we use to help understand the reason for the anomalies being found as anomalies?