Markov chain downgrades in loan book modelling
I am working on a personal loan dataset. For each loan, we recorded its credit status monthly after it was drawn by the borrower. Let’s say there were 6 status coded by A-F. My project is to use Markov chain model to train the data and estimate the transition matrix as shown below. Then we can predict the future movement of any single loan in probability.
Meanwhile, the dataset contained substantial features for a single loan, like loan amount, borrower age, income, dwelling region, bank account profile, last 90 days bank statement data, some other credit bureau data etc.
I just get this project from the very beginning and all thoughts are rough but not accurate. I need brain storm. Thanks heaps.
在給定目前狀態的情況下，如何正確使用數據來預測下一個狀態？在這個項目中應該認真考慮任何假設以及對我的任何建議或經驗？我會用 Python 和/或 R 來做。
我在另一個堆棧上回答了一個非常相似的問題，以回答有關 R 包困難的問題。我在這裡發布連結 - 我認為它會給你一條前進的道路。