Dynamic Loan Service Monitoring Using Segmented Hidden Markov Models

We describe how to apply Hidden Markov Model (HMM) to automate the loan service monitoring process. To predict the probability of defaulting in the near future, we build a statistical model of HMM from borrowers´ historical payment data. The predicted probability is dynamic in a sense that the probability keeps changing as new realized data is added to the current historical data. The time series sequence data is obtained from the composite information of the loan status and days delinquent on each month. In the training stage, various HMMs are trained: one is paid HMM and the others are defaulted HMMs. We show that more accurate monitoring can be achieved by segmenting the defaulted data and training them separately (i.e., segmented HMM method) than by training a single defaulted HMM (i.e., simple HMM method). In the prediction stage, for each active loan, we apply the following two steps: 1) classification of the loan and 2) calculation of the default probability over a prospective time period. Finally, the monitoring system sends a signal if the probability is greater than a pre-specified threshold. We also explore how to select the optimal threshold level using precision and recall analysis.